cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3660022
title: Schistosomiasis and soil-transmitted helminthiasis preventive chemotherapy: Adverse events in children from 2 to 15 years in Bengo province, Angola - Dataset

description: In the context of a targeted drug administration, between December 2012 and September 2013, we conducted two surveys after co-administrating single oral doses of praziquantel and albendazole tablets to children 2 to 15 years of age. About 24 hours after each treatment, participants answered a questionnaire about adverse events. At baseline, 605 children (55.0% male; mean age: 9.7 years) were treated; 460 were interviewed and 257 (55.9%) reported at least one adverse event, 62.3% (160/257) of children being infected with<em> schistosoma haematobium</em>. After six months of treatment, among 339 children surveyed, 184 (54.3%) reported adverse events, with 49.5% (91/184) of infected children. Adverse events were most common in preschool-aged children, with no significant difference between genders. The most frequent adverse events in the two surveys were abdominal pain (18.5%, 25.7%), headache (20.9%, 23.0%) and dizziness (15.7%, 19.8%). Children aged 12 to 15 years (adjusted OR=0.40, <em>p</em>=0.040) and those with mixed infection (adjusted OR=0.04<em>, p</em>=0.011) had lower odds of adverse events. After the second treatment, those with heavy infection (adjusted OR=2.72<em>, p</em>=0.018) and aged 9-11 years (adjusted OR=2.01, <em>p</em>=0.049) had significantly fewer adverse events. About 2.0% of children experienced severe adverse events.
DOI: 10.5281/zenodo.3660023
title: Schistosomiasis and soil-transmitted helminthiasis preventive chemotherapy: Adverse events in children from 2 to 15 years in Bengo province, Angola - Dataset

description: In the context of a targeted drug administration, between December 2012 and September 2013, we conducted two surveys after co-administrating single oral doses of praziquantel and albendazole tablets to children 2 to 15 years of age. About 24 hours after each treatment, participants answered a questionnaire about adverse events. At baseline, 605 children (55.0% male; mean age: 9.7 years) were treated; 460 were interviewed and 257 (55.9%) reported at least one adverse event, 62.3% (160/257) of children being infected with<em> schistosoma haematobium</em>. After six months of treatment, among 339 children surveyed, 184 (54.3%) reported adverse events, with 49.5% (91/184) of infected children. Adverse events were most common in preschool-aged children, with no significant difference between genders. The most frequent adverse events in the two surveys were abdominal pain (18.5%, 25.7%), headache (20.9%, 23.0%) and dizziness (15.7%, 19.8%). Children aged 12 to 15 years (adjusted OR=0.40, <em>p</em>=0.040) and those with mixed infection (adjusted OR=0.04<em>, p</em>=0.011) had lower odds of adverse events. After the second treatment, those with heavy infection (adjusted OR=2.72<em>, p</em>=0.018) and aged 9-11 years (adjusted OR=2.01, <em>p</em>=0.049) had significantly fewer adverse events. About 2.0% of children experienced severe adverse events.
DOI: 10.25919/5e3f0bd75a4e9
title: Parkes observations for project P958 semester 2019OCTS_48

description: Fast radio bursts (FRBs) continue to raise more questions than they answer. Until last year, only one burst – the only one known to repeat - had been localized to a bright radio nebula (either a young supernova remnant or pulsar wind nebulae) in a distant dwarf galaxy. Over the last two year searches with the Australian Square Kilometre Array Pathfinder (ASKAP) have detected 31 bursts, five of which have been localised to host galaxies at redshifts ranging from 0.1 to 0.5. These bursts and their hosts are very unlike the repeater, suggesting there may be a dichotomy in the population. However one of the arcminute-localised ASKAP FRBs has recently been found to repeat. Here we propose to search and study repetitions in this FRB population. Our comprehensive sample and a dense monitoring campaign of well localised bursts, at a fluence limit more than 60 times lower than that of their detections to characterise the fraction of bursts that repeat. For bursts that repeat the unique wide bandwidth observations will be used to test leading models of burst emission.
DOI: 10.25345/c5617c
title: MassIVE MSV000085856 - 073020_Mira_Pyro_Pcaledonica_Paracoccus
title: EtAc and MeOH extracts from pyrolyzed soil isolates (Paracoccus sp., Paraburkholderia caledonica) grown on ISP2 agar and char agar screened for antifungal activity against Pyronema omphalodes.
DOI: 10.5281/zenodo.3660040
title: IO Islamic 1534. Ibratnâma, First volume of the 'Ibratnâma

description: IO Islamic 1534. Ibratnâma, First volume of the ‘Ibratnâma
DOI: 10.5281/zenodo.3660039
title: IO Islamic 1534. Ibratnâma, First volume of the 'Ibratnâma

description: IO Islamic 1534. Ibratnâma, First volume of the ‘Ibratnâma
DOI: 10.5519/qd.v558bkx8
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (744196 records)
DOI: 10.5286/isis.e.rb2000138
title: Tomographic reconstruction of the shape and dimensions of LaBr3 scintillator crystals at IMAT

description: A recent project has started to provide JET (and in the future ITER tokamak) of a second real time diagnostic for fusion power, measuring the absolute intensity of the 16.7 MeV gamma line from D-T reaction. This diagnostic requires ABSOLUTE EFFICIENCY calibration of the used detector, and thus accurate measurement of the detector sensitive volume. LaBr3 detectors are the best option for high-energy gamma-ray monitoring in neutron-rich environments, but they are provided in sealed mounts that cannot be open without damage. We thus propose to perform a Neutron Tomography measurements of two such sealed detectors. One of them is a "one-of-its-kind" very large (3"x6") detector that will be made available by JET tokamak for a limited amount of time. NT will provide the exact crystal dimensions to be inserted in GEANT4-MCNP simulations for the efficiency estimation.
DOI: 10.5286/isis.e.rb2000138-1
title: Tomographic reconstruction of the shape and dimensions of LaBr3 scintillator crystals at IMAT

description: A recent project has started to provide JET (and in the future ITER tokamak) of a second real time diagnostic for fusion power, measuring the absolute intensity of the 16.7 MeV gamma line from D-T reaction. This diagnostic requires ABSOLUTE EFFICIENCY calibration of the used detector, and thus accurate measurement of the detector sensitive volume. LaBr3 detectors are the best option for high-energy gamma-ray monitoring in neutron-rich environments, but they are provided in sealed mounts that cannot be open without damage. We thus propose to perform a Neutron Tomography measurements of two such sealed detectors. One of them is a "one-of-its-kind" very large (3"x6") detector that will be made available by JET tokamak for a limited amount of time. NT will provide the exact crystal dimensions to be inserted in GEANT4-MCNP simulations for the efficiency estimation.
DOI: 10.4225/01/58197470e09b5
title: Northern Australian Aquatic Assets Geodatabase v2.0

description: The Northern Australian Aquatic Assets Geodatabase (NAAAG) v2.0 has been developed to assist in the quantification and communication of risks associated with threats to aquatic ecological assets across northern Australia. The NAAAG comprises a base level set of spatial layers on aquatic features (rivers, lakes, swamps, estuaries, springs) and context layers (catchments, land use, protected areas, terrain, vegetation, roads and places) associated with the aquatic features. The NAAAG also includes a catchment based assessment of relative risk scores associated with threats arising from factors such as flow alteration, sea level rise and climate change. Because a great deal of spatial data is now freely available for download, the NAAAG is not a comprehensive database but has been designed as a base level set of data on aquatic features upon which users can incorporate additional data sets if desired. The NAAAG v2.0 data package has been developed using ESRI Geodatabase technologies and the data layers have been set up for viewing as an ArcMAP 10.1 project. A User Guide for the NAAAG, provides information on the design and structure of the geodatabase, how to use the data, and background information on data layers stored in the geodatabase. Metadata and supporting documentation for the data sets incorporated into the geodatabases is also included in the data package.
DOI: 10.5281/zenodo.3634817
title: Rebel Media contributors timeline

description: Data on the tenures of every person to have appeared on Rebel Media's masthead. Start dates for each contributor's tenure are cleaned up from the Wayback Machine's caches of the Rebel Media masthead list, formerly located at therebel.media/therebels before the website changed its top-level domain to rebelnews.com. The first cached copy is from February 15th 2015, which is the same day that the Rebel posted their first YouTube video. The most recent copy of the list that is mapped is a cached copy from May 17th 2019. End dates from this data have been adjusted based on news reports of hosts' departures where applicable.
description: Pages were ripped with hartator's wayback-machine-downloader ruby script: https://github.com/hartator/wayback-machine-downloader
description: {"references": ["Internet Archive. 2020. 'Archive of \"The Rebels\" at https://www.therebel.Media/therebels'. The Wayback Machine. 8 February 2020. https://web.archive.org/web/20190202000000*/https://www.therebel.media/therebels."]}
DOI: 10.5281/zenodo.3634816
title: Rebel Media contributors timeline

description: Data on the tenures of every person to have appeared on Rebel Media's masthead. Start dates for each contributor's tenure are cleaned up from the Wayback Machine's caches of the Rebel Media masthead list, formerly located at therebel.media/therebels before the website changed its top-level domain to rebelnews.com. The first cached copy is from February 15th 2015, which is the same day that the Rebel posted their first YouTube video. The most recent copy of the list that is mapped is a cached copy from May 17th 2019. End dates from this data have been adjusted based on news reports of hosts' departures where applicable.
description: Pages were ripped with hartator's wayback-machine-downloader ruby script: https://github.com/hartator/wayback-machine-downloader
description: {"references": ["Internet Archive. 2020. 'Archive of \"The Rebels\" at https://www.therebel.Media/therebels'. The Wayback Machine. 8 February 2020. https://web.archive.org/web/20190202000000*/https://www.therebel.media/therebels."]}
DOI: 10.17632/cyxsxdgskr.1
title: Data for: Analysis of cracking in general composite laminates subject to triaxial and bending loads

description: The files involve appendices and also excel files regarding the numerical results.
DOI: 10.6084/m9.figshare.3761562.v1383
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.5281/zenodo.3659310
title: Sensitivity of atmospheric river vapor transport and precipitation to uniform sea-surface temperature increases

description: This is the companion data for the manuscript of the same title, submitted to JGR: Atmospheres on 02/08/2020. <strong>modelParameters: </strong>this folder contains the scripts I ran on NERSC Cori in 2019 to initialize the CESM2.0/CAM5 model runs. qobs_script_newcase.sh: creates all cases, for the Baseline as well as the +xK SST runs docn_comp_mod.F90: original CAM5 aquaplanet SST distributions; "QOBS" is used for my "Baseline" experiments plusxK_docn_comp_mod.F90: modified SST distributions, for x=(2,4,6); these are simply uniform additions to the QOBS SST distributions Macros.make & env_mach_specific.xml: configuration files to run CESM2.0 on Cori at the time user_nl_cam: namelist for CAM5; specifies some model run parameters as well as output variables. <strong>detectionParameters: </strong>this folder contains the scripts I ran on NERSC Cori in 2019 to detect tropical cyclones and atmospheric rivers. Use this code for reference purposes only (i.e., to see which parameters were used to detect ARs or TCs). It will not run as-is. <strong>All other subfolders </strong>provide working examples of code used to generate figures (all in Jupyter notebooks) for the manuscript; folder names are descriptive. Unfortunately, model output was large (~12 TB). Hence, I only provide mean data, used directly to generate figures, in this repository. All model output are archived on tape at NERSC. For more details, refer to the manuscript, or contact me (eelliott@ucdavis.edu).
DOI: 10.6084/m9.figshare.8194085.v2
title: Makowicz, Murray, Schlupp - Animal Behavior.xlsx

description: Female-female aggression of Poecilia latipinna towards conspecifics and heterospecific Poecilia formosa when in front of different audiences.
DOI: 10.5281/zenodo.3653436
title: Discussion Survey from Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes

description: These files include summaries of pre-meeting survey of important topics to discuss at the Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes The Chapman Conference was supported by NASA Grants: 936723.02.01.09.14 and 936723.02.01.11.21 and by NSF Award AGS 1848885
DOI: 10.5281/zenodo.3653435
title: Discussion Survey from Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes

description: These files include summaries of pre-meeting survey of important topics to discuss at the Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes The Chapman Conference was supported by NASA Grants: 936723.02.01.09.14 and 936723.02.01.11.21 and by NSF Award AGS 1848885
DOI: 10.21227/9j97-xf37
title: Date Fruit Dataset for Automated Harvesting and Visual Yield Estimation

description: The date fruit dataset was created to address the requirements of many applications in the pre-harvesting and harvesting stages. The two most important applications are automatic harvesting and visual yield estimation. The dataset is divided into two subsets and each of them is oriented into one of these two applications. The first dataset consists of 8079 images of more than 350 date bunches captured from 29 date palms. The date bunches belong to five date types: Naboot Saif, Khalas, Barhi, Meneifi, and Sullaj. The pictures of date bunches were captured using a color camera in six imaging sessions. The imaging sessions covered all date maturity stages: immature, Khalal, Rutab, and Tamar. The dataset is provided with a large degree of variations to reflect the challenges in natural environments and date fruit orchards. These variations in images include different angels and scales, different daylight conditions having poor illumination images, and date bunches covered by bags. The dataset is fully labeled according to type, maturity, and harvesting decision. We can use this dataset in many applications including fruit detection, segmentation, classification, maturity analysis, and automatic harvesting. The second dataset contains images, videos, and weight measurements to help in many applications such as yield estimation. In this dataset, we marked date bunches for selected palms, recorded 360° video for each palm, and measured their data (height, trunk circumference, total yield, number of bunches, and weight of bunches). We also captured images of each bunch from different angles before harvesting and on a graph paper after harvesting. Both datasets have been arranged with a coding scheme to simplify referring, linking, and facilitating future extensions. If you use the dataset, please cite the following articles:[1] H. Altaheri, M. Alsulaiman, G. Muhammad, S. U. Amin, M. Bencherif, and M. Mekhtiche, “Date Fruit Dataset for Intelligent Harvesting", Data in Brief, vol. 26, p. 104514, Oct. 2019.[2] H. Altaheri, M. Alsulaiman, and G. Muhammad, "Date Fruit Classification for Robotic Harvesting in a Natural Environment Using Deep Learning", IEEE Access, vol. 7, pp. 117115-117133, 2019. Update: Dataset-2 was uploaded. Date: 16/05/2019. The pdf files of dataset documentation and experiment papers were attached. Date: 23/10/2019. New zip file, named "DATASET-1 (224 X 224) Categorized.zip", was uploaded. This file contains the images of dataset-1 that resized to 224x224 pixels and categorized into subfolders according to date type, maturity, and the harvesting decision. This categorization, and image resolution, is related to the experiments described in ref [2]. Date: 09/02/2020.
DOI: 10.21227/brfs-4m25
title: Date Fruit Dataset for Automated Harvesting and Visual Yield Estimation

description: The date fruit dataset was created to address the requirements of many applications in the pre-harvesting and harvesting stages. The two most important applications are automatic harvesting and visual yield estimation. The dataset is divided into two subsets and each of them is oriented into one of these two applications. The first dataset consists of 8079 images of more than 350 date bunches captured from 29 date palms. The date bunches belong to five date types: Naboot Saif, Khalas, Barhi, Meneifi, and Sullaj. The pictures of date bunches were captured using a color camera in six imaging sessions. The imaging sessions covered all date maturity stages: immature, Khalal, Rutab, and Tamar. The dataset is provided with a large degree of variations to reflect the challenges in natural environments and date fruit orchards. These variations in images include different angels and scales, different daylight conditions having poor illumination images, and date bunches covered by bags. The dataset is fully labeled according to type, maturity, and harvesting decision. We can use this dataset in many applications including fruit detection, segmentation, classification, maturity analysis, and automatic harvesting. The second dataset contains images, videos, and weight measurements to help in many applications such as yield estimation. In this dataset, we marked date bunches for selected palms, recorded 360° video for each palm, and measured their data (height, trunk circumference, total yield, number of bunches, and weight of bunches). We also captured images of each bunch from different angles before harvesting and on a graph paper after harvesting. Both datasets have been arranged with a coding scheme to simplify referring, linking, and facilitating future extensions. If you use the dataset, please cite the following articles:[1] H. Altaheri, M. Alsulaiman, G. Muhammad, S. U. Amin, M. Bencherif, and M. Mekhtiche, “Date Fruit Dataset for Intelligent Harvesting", Data in Brief, vol. 26, p. 104514, Oct. 2019.[2] H. Altaheri, M. Alsulaiman, and G. Muhammad, "Date Fruit Classification for Robotic Harvesting in a Natural Environment Using Deep Learning", IEEE Access, vol. 7, pp. 117115-117133, 2019. Updates: Dataset-2 was uploaded. Date: 16/05/2019. The pdf files of dataset documentation and experiment papers were attached. Date: 23/10/2019. New zip file, named "DATASET-1 (224 X 224) Categorized.zip", was uploaded. This file contains the images of dataset-1 that resized to 224x224 pixels and categorized into subfolders according to date type, maturity, and the harvesting decision. This categorization, and image resolution, is related to the experiments described in ref [2]. Date: 09/02/2020.
DOI: 10.21227/nb1w-hv39
title: Date Fruit Dataset for Automated Harvesting and Visual Yield Estimation

description: The date fruit dataset was created to address the requirements of many applications in the pre-harvesting and harvesting stages. The two most important applications are automatic harvesting and visual yield estimation. The dataset is divided into two subsets and each of them is oriented into one of these two applications. The first dataset consists of 8079 images of more than 350 date bunches captured from 29 date palms. The date bunches belong to five date types: Naboot Saif, Khalas, Barhi, Meneifi, and Sullaj. The pictures of date bunches were captured using a color camera in six imaging sessions. The imaging sessions covered all date maturity stages: immature, Khalal, Rutab, and Tamar. The dataset is provided with a large degree of variations to reflect the challenges in natural environments and date fruit orchards. These variations in images include different angels and scales, different daylight conditions having poor illumination images, and date bunches covered by bags. The dataset is fully labeled according to type, maturity, and harvesting decision. We can use this dataset in many applications including fruit detection, segmentation, classification, maturity analysis, and automatic harvesting. The second dataset contains images, videos, and weight measurements to help in many applications such as yield estimation. In this dataset, we marked date bunches for selected palms, recorded 360° video for each palm, and measured their data (height, trunk circumference, total yield, number of bunches, and weight of bunches). We also captured images of each bunch from different angles before harvesting and on a graph paper after harvesting. Both datasets have been arranged with a coding scheme to simplify referring, linking, and facilitating future extensions. If you use the dataset, please cite the following articles:[1] H. Altaheri, M. Alsulaiman, G. Muhammad, S. U. Amin, M. Bencherif, and M. Mekhtiche, “Date Fruit Dataset for Intelligent Harvesting", Data in Brief, vol. 26, p. 104514, Oct. 2019.[2] H. Altaheri, M. Alsulaiman, and G. Muhammad, "Date Fruit Classification for Robotic Harvesting in a Natural Environment Using Deep Learning", IEEE Access, vol. 7, pp. 117115-117133, 2019. Updates: Dataset-2 was uploaded. Date: 16/05/2019. The pdf files of dataset documentation and experiment papers were attached. Date: 23/10/2019. New zip file, named "DATASET-1 (224 X 224) Categorized.zip", was uploaded. This file contains the images of dataset-1 that resized to 224x224 pixels and categorized into subfolders according to date type, maturity, and the harvesting decision. This categorization, and image resolution, is related to the experiments described in ref [2]. Date: 09/02/2020.