cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.11924487
title: Additional file 12 of Metagenomic analysis reveals the microbiome and resistome in migratory birds

description: Additional file 12: Table S4. ARGs abundance in 100 samples from different bird species.
DOI: 10.6084/m9.figshare.11924487.v1
title: Additional file 12 of Metagenomic analysis reveals the microbiome and resistome in migratory birds

description: Additional file 12: Table S4. ARGs abundance in 100 samples from different bird species.
DOI: 10.6084/m9.figshare.11924499
title: Additional file 13 of Metagenomic analysis reveals the microbiome and resistome in migratory birds

description: Additional file 13: Table S5. ARG types present in all bird species.
DOI: 10.6084/m9.figshare.11924499.v1
title: Additional file 13 of Metagenomic analysis reveals the microbiome and resistome in migratory birds

description: Additional file 13: Table S5. ARG types present in all bird species.
DOI: 10.6084/m9.figshare.11924508
title: Additional file 14 of Metagenomic analysis reveals the microbiome and resistome in migratory birds

description: Additional file 14: Table S6. The 10 most abundant ARG types present in each bird species.
DOI: 10.6084/m9.figshare.11924508.v1
title: Additional file 14 of Metagenomic analysis reveals the microbiome and resistome in migratory birds

description: Additional file 14: Table S6. The 10 most abundant ARG types present in each bird species.
DOI: 10.6084/m9.figshare.11924520.v1
title: Additional file 15 of Metagenomic analysis reveals the microbiome and resistome in migratory birds

description: Additional file 15: Table S7. Network properties on co-occurrence patterns of ARG types in bird populations.
DOI: 10.6084/m9.figshare.11924520
title: Additional file 15 of Metagenomic analysis reveals the microbiome and resistome in migratory birds

description: Additional file 15: Table S7. Network properties on co-occurrence patterns of ARG types in bird populations.
DOI: 10.6084/m9.figshare.11924526
title: Additional file 16 of Metagenomic analysis reveals the microbiome and resistome in migratory birds

description: Additional file 16: Table S8. Network properties on co-occurrence patterns between ARG types and micriobial taxa in bird populations.
DOI: 10.6084/m9.figshare.11924526.v1
title: Additional file 16 of Metagenomic analysis reveals the microbiome and resistome in migratory birds

description: Additional file 16: Table S8. Network properties on co-occurrence patterns between ARG types and micriobial taxa in bird populations.
DOI: 10.6084/m9.figshare.11924532
title: Additional file 17 of Metagenomic analysis reveals the microbiome and resistome in migratory birds

description: Additional file 17: Table S9. Detailed information of sequencing depth and antibiotic resistance gene number in 99 samples from bird and its associated environments.
DOI: 10.6084/m9.figshare.11924532.v1
title: Additional file 17 of Metagenomic analysis reveals the microbiome and resistome in migratory birds

description: Additional file 17: Table S9. Detailed information of sequencing depth and antibiotic resistance gene number in 99 samples from bird and its associated environments.
DOI: 10.25345/c5j10t
title: MassIVE MSV000085046 - Fungi_with_Bacteria_IKproject_MS1
title: MS1 collected on qTOF, UPLC C18 gradient of 10-100% ACN over first 12 minutes, held at 100% ACN for minute 12-14. Fungi and Bacteria grown alone and pairwise on Cheese Curd Agar, naming is fungi first then bacterial partner (if it is a pairwise culture), then biological replicate number. CCA = cheese curd agar media control Ecoli = E. coli K12 JB418 = Pseudomonas psychrophila sp. JB418 12 = Penicillium solitum SAM = Penicillium camemberti RS17 = Penicillium atramentosum Fus = Fusarium domesticum JB370 = Scopulariopsis strain JB370 1655 = Scopulariopsis strain 165-5 Can = Candida catenulata Deb = Debaryomyces hansenii
DOI: 10.6084/m9.figshare.11924589
title: Additional file 9 of Metagenomic analysis reveals the microbiome and resistome in migratory birds

description: Additional file 9: Table S1. The detailed information of sample collection.
DOI: 10.6084/m9.figshare.11924589.v1
title: Additional file 9 of Metagenomic analysis reveals the microbiome and resistome in migratory birds

description: Additional file 9: Table S1. The detailed information of sample collection.
DOI: 10.25345/c5d99r
title: MassIVE MSV000085047 - Analysis of metabolites in vinegar using UPLC-Qtof-MS
title: Untargeted analysis of vinegar metabolites from China and Japan
DOI: 10.25345/c58h6f
title: MassIVE MSV000085048 - Bile acid dehydroxylation by the gut microbiome
title: A metabolic pathway for bile acid dehydroxylation by the gut microbiome
DOI: 10.6073/pasta/664e1cbe61bab095e8d7da15d757c9b9
title: Annual seasonality trends of Normalized Difference Vegetation Index (NDVI), Maricopa County, Arizona, 2001-2018

description: Description: A dataset with 18 years (2001-2018) of consistent, spatial and temporal patterns of Normalized Difference Vegetation Index (NDVI) values in Maricopa County AZ Abstract: This dataset consists of 18 years (2001-2018) of consistent, spatial, and temporal patterns of vegetation indices, as expressed by the Normalized Difference Vegetation Index (NDVI), in Maricopa County, AZ. I download and process images, at 250m resolution, from the Moderate Resolution Imaging Spectroradiometer (MODIS). MODIS uses the atmospherically-corrected reflectance in the red, near-infrared, and blue wavebands to calculate vegetation indices. In the last decades, vegetation indices have been widely used for monitoring the seasonal variation of vegetation, document vegetation structure, productivity, overall health, and land cover changes and measure vegetation productivity in desert landscapes. NDVI is also of high interest to investigate the potential distribution of plant and animal species and to investigate the effect of climate change on vegetation productivity. MODIS data were obtained from https://lpdaac.usgs.gov, maintained by the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC) at the USGS Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota. Spatial Extent: Maricopa County, AZ, USA.
DOI: 10.25919/5e5d869e0af6a
title: Parkes observations for project P455 semester 2019OCTS_04

description: This project is to continue timing and profile studies of the first double-pulsar system, a unique laboratory for gravitational physics. Recent results, including the first measurements of higher-order light-propagation effects and of the relativistic deformation of the orbit, highlight the importance of a long term observational campaign for this remarkable system. The main aims of this proposal are to provide the strongest tests to date for general relativity and to measure for the first time the moment-of-inertia of a neutron star. Additionally, we will determine the system geometry and map the pulsar beams via geodetic precession. Whenever possible, we will exploit the high sensitivity of the new UWL receiver.
DOI: 10.25919/5e5d869e0b3d6
title: Parkes observations for project PX500 semester 2019OCTS_11

description: This proposal relates to time purchased by the Chinese Academy of Sciences for Parkes follow-up confirmation and timing of FAST discoveries. As the observing time has been purchased this proposal (and the related PX501) does not require TAC grading. The primary reason for submitting a proposal is to ensure an outreach statement and that the proposal is entered into OPAL for the data archive to access. The Chinese team have two time requests. The time scheduled for the first, relating to this proposal, will be allocated in blocks of 24 hours and the data will become available in the data archive with an 18 month embargo period. The second type of time will be scheduled in shorter blocks and the data archived with a 10 year embargo.
DOI: 10.25919/5e5d86ead794e
title: Parkes observations for project P737 semester 2019OCTS_05

description: The aim of this proposal is to carry out tests of the new backend instruments which cannot be carried out during standard maintenance periods. The scheduled time is used to measure beam shapes, determine polarisation calibration solutions and commission the National Facility observing modes for the new wide-band receiver.
DOI: 10.17632/jt9r4bknsw.1
title: Data for: CONCEPTUALISATION OF THE THERAPEUTIC ALLIANCE IN PHYSIOTHERAPY: IS IT ADEQUATE?

description: The attached document contains study appendices: 1. Data corpus 2. Attribute codes 3. Antecedent codes 4. Consequence codes
DOI: 10.6073/pasta/f882f10ac65f1bbbb30a0db3ae412a5d
title: Annual seasonality trends of Enhanced Vegetation Index (EVI), Maricopa County, Arizona, 2001-2018

description: Description: A dataset with 18 years (2001-2018) of consistent, spatial and temporal patterns of enhanced vegetation index (EVI) values in Maricopa County AZ Abstract: This dataset consists of 18 years (2001-2018) of consistent, spatial, and temporal patterns of vegetation indices, as expressed by the enhanced vegetation index (EVI), in Maricopa County, AZ. I download and process images, at 250m resolution, from the Moderate Resolution Imaging Spectroradiometer (MODIS). MODIS uses the atmospherically-corrected reflectance in the red, near-infrared, and blue wavebands to calculate vegetation indices. In the last decades, vegetation indices have been widely used for monitoring the seasonal variation of vegetation, document vegetation structure, productivity, overall health, and land cover changes and measure vegetation productivity in desert landscapes. EVI is also of high interest to investigate the potential distribution of plant and animal species and to investigate the effect of climate change on vegetation productivity. MODIS data were obtained from https://lpdaac.usgs.gov, maintained by the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC) at the USGS Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota. Spatial Extent: Maricopa County, AZ, USA.
DOI: 10.17632/wnpzyb59pp.1
title: Data for: Quantitative spore analysis of the E2a and E2b goniatite zones (Arnsbergian, Mississippian) in mudstones from the Southern Pennine Basin (U.K.).

description: The full palynological counts for the Carsington Dam Reconstruction C3 Core (Appendix 1), the Karenight-1 Core (Appendix 2) and the input data for the Detrended Correspondence Analysis.
DOI: 10.17632/gvpy69796n.3
title: Emissions data from geothermal energy exploitation in Italy

description: The data reported here originated from the sampling activities carried on by the Regional Agency for Environmntal Protection of Tuscany (Italy). The reports and results of the sampling activities are pubblicly available on the ARPAT website at the following link: http://www.arpat.toscana.it/documentazione/report/report-geotermia Data is available starting from 2000 up to date, when new information will be made available this dataset will be updated. This dataset does not report raw data but the information is manipulated by means of basic arithmetic in order to obtain clearer tables than the original ones. This dataset is related to: https://doi.org/10.1016/j.dib.2019.104339
DOI: 10.25919/5e5dcffb232a9
title: Parkes observations for project P958 semester 2019OCTS_62

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: 10.17632/vptywz7dpr.3
title: Data for: Effect of dexmedetomidine sedation on the swallowing reflex: A pilot study

description: Effect of dexmedetomidine sedation on the swallowing reflex
DOI: 10.25345/c54t35
title: MassIVE MSV000085049 - Analysis of Immune Checkpoint Drug Targets and Tumor Proteotypes in Non-small Cell Lung Cancer
title: Proteome analysis of non-small cell lung cancer from 26 formalin-fixed, paraffin-embedded tumor specimens
DOI: 10.26182/5e5dd3ed835fa
title: Osteoclast-specific eQTL results for osteoporosis risk variants (co-localised loci)
DOI: 10.17632/fyz8x9jycb.1
title: Data for: Microstructure controlling and dielectric relaxation dynamic of ZIF-8 synthesized in different solvents

description: Original data from XRD and precision impedance analyzer.
DOI: 10.11577/1602169
title: Comparing serial X-ray crystallography and microcrystal electron diffraction (MicroED) as methods for routine structure determination from small macromolecular crystals

description: RT XFEL structure of CypA solved using MESH injection system
DOI: 10.6084/m9.figshare.11924466
title: Additional file 10 of Metagenomic analysis reveals the microbiome and resistome in migratory birds

description: Additional file 10: Table S2. Detailed information of sequencing depth and antibiotic resistance gene number in 100 samples from different bird species.
DOI: 10.6084/m9.figshare.11924466.v1
title: Additional file 10 of Metagenomic analysis reveals the microbiome and resistome in migratory birds

description: Additional file 10: Table S2. Detailed information of sequencing depth and antibiotic resistance gene number in 100 samples from different bird species.
DOI: 10.26066/rds.21976
title: Poverty and Vulnerability Assessment Tool (PVAT) questionnaire dataset of Nepal 2011/12

description: The Poverty and Vulnerability Assessment Tool (PVAT) dataset was developed to capture the micro-level perspective of people's experiences with poverty and vulnerability and to monitor poverty and vulnerability trends on the ground. The assessment was based on a standardized questionnaire that has been developed to assess, describe, and analyse the situation of poor and vulnerable people in the Nepal. This will lead to an enhanced understanding of multiple aspects of livelihood assets, structures, and the processes that underpin such assets. It will also help determine levels of access, terms of exchange, and returns from livelihood assets as well as livelihood strategies and their outcomes for the poor. PVAT was conducted twice in 2011 and 2012.
DOI: 10.26066/rds.9182
title: Basin Boundary of Koshi River Basin

description: Digital line dataset of Basin Boundary of Koshi Basin. This dataset is created using Topographic sheet data at scale of 25,000/50,000, of 1995 acquired from National Geographic Information Infrastructure Project (NGIIP), Dept. of Survey, Kathmandu, Nepal and ESRI Global dataset.
DOI: 10.26066/rds.34424
title: Landslide data of Koshi basin (within Nepal) of 1960 developed through remote sensing approach

description: Landslide data of Koshi basin was digitized from toposheet maps published in 1960s.
DOI: 10.26066/rds.34425
title: Landslide data of Koshi basin (within Nepal) of 1990 developed through remote sensing approach

description: Landslide data of Koshi basin is digitized from topo sheets published in 1990s.
DOI: 10.26066/rds.34426
title: Landslide data of Koshi basin (within Nepal) of 2010 developed through remote sensing approach

description: Landslide data of Koshi basin was digitized from Landsat and Googel Earth.
DOI: 10.26066/rds.31016
title: Landslide data of 14 earthquake affected districts of Nepal

description: Nepal earthquake of 2015 resulted in activation and re-activation of many landslides in and around epi-center and after shock areas. The landslide inventory was prepared by visually digitizing landslide polygons on Google Earth Pro. The earthquake database contains both pre-event landslide and co-siesmic landslides.
DOI: 10.26066/rds.31929
title: Particulate matter (PM) in Kathmandu Valley from 2002-2008

description: Data on ambient PM 10 levels from six locations in the Kathmandu Valley recorded by means of continuous sampling using low volume air samplers from October 2002 to March 2007 were used to investigate PM 10 concentration dynamics in the valley. This study analyzed daily patterns and dynamics of PM 2.5 concentrations in the Kathmandu Valley during three winters. The PM 2.5 data showed a daily repetitive cycle which represents influence of local air flow and dispersion and accumulation of air pollutants in the valley.
DOI: 10.26066/rds.9355
title: Clean-ice and Debris-covered Glaciers of Bhutan 1980

description: Digital polygon data of Glaciers of Bhutan in 1980. This dataset is created using Landsat MSS imageries of 1980. The glacier outlines was derived semi-automatically using object-based image classification (OBIC ) method separately for clean ice and debris cover and further editing and validation was done carefully by draping over the high resolution images from Google Earth.
DOI: 10.26066/rds.9356
title: Clean-ice and Debris-covered Glaciers of Bhutan 1990

description: Digital polygon data of Glaciers of Bhutan in 1990. This dataset is created using Landsat MSS and TM imageries of 1990. The glacier outlines was derived semi-automatically using object-based image classification (OBIC ) method separately for clean ice and debris cover and further editing and validation was done carefully by draping over the high resolution images from Google Earth.
DOI: 10.26066/rds.9357
title: Clean-ice and Debris-covered Glaciers of Bhutan 2000

description: Digital polygon data of Glaciers of Bhutan in 2000. This dataset is created using Landsat TM and ETM+, imageries of 2000. The glacier outlines was derived semi-automatically using object-based image classification (OBIC ) method separately for clean ice and debris cover and further editing and validation was done carefully by draping over the high resolution images from Google Earth.
DOI: 10.26066/rds.9358
title: Clean-ice and Debris-covered Glaciers of Bhutan 2010

description: Digital polygon data of Glaciers of Bhutan in 2010. This dataset is created using Landsat TM and ETM+, imageries of 2010. The glacier outlines was derived semi-automatically using object-based image classification (OBIC ) method separately for clean ice and debris cover and further editing and validation was done carefully by draping over the high resolution images from Google Earth.
DOI: 10.26066/rds.9359
title: Status of Glaciers in Hindu Kush Himalayan (HKH) Region

description: Digital polygon data of Status of Glaciers in Hindu Kush Himalayan (HKH) Region during 2005 ± 3 (2002-2008) years. This dataset is created using Landsat ETM+ imageries of respective years. The glacier outlines was derived semi-automatically using object-based image classification (OBIC ) method separately for clean ice and debris cover and further editing and validation was done carefully by draping over the high resolution images from Google Earth. The attribute data were assigned to each glacier using 90m resolution SRTM DEM. Source: ICIMOD and CAREERI (data for the Chinese part of the HKH region is a product of a national project of the Ministry of Science and Technology of China (Grant no. 2006FY110200))
DOI: 10.26066/rds.26858
title: Meteorological data from Rikha Samba automatic weather station

description: Meteorological data from Rikha Samba, Hidden Valley, Nepal
DOI: 10.26066/rds.27309
title: Lirung UAV-derived orthomosaic, October 2013

description: Orthomosaic of Lirung Glacier produced from unmanned aerial vehicle imagery using the Structure from Motion algorithm. The data is georeferenced using ground control points measured with differential GPS.
DOI: 10.26066/rds.27284
title: Lirung UAV-derived orthomosaic, May 2013

description: Orthomosaic of Lirung Glacier produced from unmanned aerial vehicle imagery using the Structure from Motion algorithm. The data is georeferenced using ground control points measured with differential GPS.
DOI: 10.26066/rds.27264
title: Lirung UAV-derived elevation model, October 2013

description: Elevation model of Lirung Glacier produced from unmanned aerial vehicle imagery using the Structure from Motion algorithm. The data is georeferenced using ground control points measured with differential GPS.
DOI: 10.26066/rds.27224
title: Lirung UAV-derived elevation model, May 2013

description: Elevation model of Lirung Glacier produced from unmanned aerial vehicle imagery using the Structure from Motion algorithm. The data is georeferenced using ground control points measured with differential GPS.
DOI: 10.26066/rds.8899
title: Active Fire Incidents in Nepal

description: Moderate Resolution Imaging Spectroradiometer (MODIS) based active fire incident location data in Nepal from February 2012 to June 2016. The active fire data has been overlaid with land cover data of Nepal to find out forest fire incidents in the country.
DOI: 10.26066/rds.9149
title: Land Cover in 1990 of Central Karakoram National Park(CKNP), Pakistan.

description: Digital dataset of Land Cover in 1990 of Central Karakoram National Park(CKNP) area, Pakistan. This dataset is extracted from LandSat satellite imagery of 2000 with spatial resolution of 30 m and spectral resolution of 6 bands. Land cover classes are classified using Nearest Neighbor method.
DOI: 10.26066/rds.9148
title: Land Cover in 2000 of Central Karakoram National Park(CKNP), Pakistan.

description: Digital dataset of Land Cover in 2000 of Central Karakoram National Park(CKNP) area, Pakistan. This dataset is extracted from LandSat satellite imagery of 2000 with spatial resolution of 30 m and spectral resolution of 6 bands. land cover classes are classified using Nearest Neighbor method.
DOI: 10.26066/rds.9147
title: Land Cover in 2010 in Central Karakoram National Park (CKNP), Pakistan.

description: Digital dataset of Land Cover in 2010 of Central Karakoram National Park (CKNP) area, Pakistan. This dataset is extracted from LandSat satellite imagery of 2010 with spatial resolution of 30 m and spectral resolution of 6 bands. Land cover classes are classified using Nearest Neighbor method.
DOI: 10.26066/rds.9154
title: Land Cover Change between 2000-2010 in Central Karakoram National Park(CKNP), Pakistan.

description: Digital dataset of Land Cover change between 2000 and 2010 of Central Karakoram National Park (CKNP) area, Pakistan. This dataset is derived from the LandSat satellite imagery of 2000 and 2010 to show the change in land cover classes.
DOI: 10.26066/rds.9152
title: Land Cover Change between 1990-2000 in Central Karakoram National Park(CKNP), Pakistan.

description: Digital dataset of Land Cover change between 1990 and 2000 of Central Karakoram National Park (CKNP) area, Pakistan. This dataset is derived from the LandSat satellite imagery of 1990 and 2000 to show the change in land cover classes.
DOI: 10.26066/rds.9153
title: Land Cover Change between 1990-2010 in Central Karakoram National Park(CKNP), Pakistan.

description: Digital dataset of Land Cover change between 1990 and 2010 of Central Karakoram National Park (CKNP) area, Pakistan. This dataset is derived from the LandSat satellite imagery of 1990 and 2010 to show the change in land cover classes.
DOI: 10.26066/rds.28309
title: Land cover of Eastern Himalayas 1975

description: A land cover change is a significant contributor to environmental change. Comparable, harmonized and standardized land cover data at different dates are required to assess the land cover change. In this regard, regional land cover data of Eastern Himalayas for 1975 was developed by the International Centre for Integrated Mountain Development (ICIMOD) using Landsat MSS images of 1975.