cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.1805609
title: Geomorpho90m, empirical evaluation and accuracy assessment of global high-resolution geomorphometric layers

description: Topographical relief comprises the vertical and horizontal variations of the Earth’s terrain and drives processes in geomorphology, biogeography, climatology, hydrology and ecology. Its characterisation and assessment, through geomorphometry and feature extraction, is fundamental to numerous environmental modelling and simulation analyses. We, therefore, developed the Geomorpho90m global dataset comprising of different geomorphometric features derived from the MERIT-Digital Elevation Model (DEM) - the best global, high-resolution DEM available. The fully-standardised 26 geomorphometric variables consist of layers that describe the (i) rate of change across the elevation gradient, using first and second derivatives, (ii) ruggedness, and (iii) geomorphological forms. The Geomorpho90m variables are available at 3 (~90 m) and 7.5 arc-second (~250 m) resolutions under the WGS84 geodetic datum, and 100 m spatial resolution under the Equi7 projection. They are useful for modelling applications in fields such as geomorphology, geology, hydrology, ecology and biogeography. Publication https://www.nature.com/articles/s41597-020-0479-6
description: Geomorphometric layers base on MERIT-DEM computed at 100 m under Equi7 projection and then re-sampled at 7.5 arc-second (~250m) under WGS84 geographic coordinate system. Higher resolution (~90m) available at https://portal.opentopography.org/dataspace/dataset?opentopoID=OTDS.012020.4326.1 Technical documentation available at http://www.spatial-ecology.net/dokuwiki/doku.php?id=topovar90m
DOI: 10.5281/zenodo.1805610
title: Geomorpho90m, empirical evaluation and accuracy assessment of global high-resolution geomorphometric layers

description: Topographical relief comprises the vertical and horizontal variations of the Earth’s terrain and drives processes in geomorphology, biogeography, climatology, hydrology and ecology. Its characterisation and assessment, through geomorphometry and feature extraction, is fundamental to numerous environmental modelling and simulation analyses. We, therefore, developed the Geomorpho90m global dataset comprising of different geomorphometric features derived from the MERIT-Digital Elevation Model (DEM) - the best global, high-resolution DEM available. The fully-standardised 26 geomorphometric variables consist of layers that describe the (i) rate of change across the elevation gradient, using first and second derivatives, (ii) ruggedness, and (iii) geomorphological forms. The Geomorpho90m variables are available at 3 (~90 m) and 7.5 arc-second (~250 m) resolutions under the WGS84 geodetic datum, and 100 m spatial resolution under the Equi7 projection. They are useful for modelling applications in fields such as geomorphology, geology, hydrology, ecology and biogeography. Publication https://www.nature.com/articles/s41597-020-0479-6
description: Geomorphometric layers base on MERIT-DEM computed at 100 m under Equi7 projection and then re-sampled at 7.5 arc-second (~250m) under WGS84 geographic coordinate system. Higher resolution (~90m) available at https://portal.opentopography.org/dataspace/dataset?opentopoID=OTDS.012020.4326.1 Technical documentation available at http://www.spatial-ecology.net/dokuwiki/doku.php?id=topovar90m
DOI: 10.5281/zenodo.1807124
title: Geomorpho90m, empirical evaluation and accuracy assessment of global high-resolution geomorphometric layers

description: Topographical relief comprises the vertical and horizontal variations of the Earth’s terrain and drives processes in geomorphology, biogeography, climatology, hydrology and ecology. Its characterisation and assessment, through geomorphometry and feature extraction, is fundamental to numerous environmental modelling and simulation analyses. We, therefore, developed the Geomorpho90m global dataset comprising of different geomorphometric features derived from the MERIT-Digital Elevation Model (DEM) - the best global, high-resolution DEM available. The fully-standardised 26 geomorphometric variables consist of layers that describe the (i) rate of change across the elevation gradient, using first and second derivatives, (ii) ruggedness, and (iii) geomorphological forms. The Geomorpho90m variables are available at 3 (~90 m) and 7.5 arc-second (~250 m) resolutions under the WGS84 geodetic datum, and 100 m spatial resolution under the Equi7 projection. They are useful for modelling applications in fields such as geomorphology, geology, hydrology, ecology and biogeography. Publication https://www.nature.com/articles/s41597-020-0479-6
description: Geomorphometric layers (geomorphological forms) base on MERIT-DEM computed at 100 m under Equi7 projection and then re-sampled at 7.5 arc-second (~250m) under WGS84 geographic coordinate system. Higher resolution (~90m) available at https://portal.opentopography.org/dataspace/dataset?opentopoID=OTDS.012020.4326.1 Technical documentation available at http://www.spatial-ecology.net/dokuwiki/doku.php?id=topovar90m
DOI: 10.5281/zenodo.1807125
title: Geomorpho90m, empirical evaluation and accuracy assessment of global high-resolution geomorphometric layers

description: Topographical relief comprises the vertical and horizontal variations of the Earth’s terrain and drives processes in geomorphology, biogeography, climatology, hydrology and ecology. Its characterisation and assessment, through geomorphometry and feature extraction, is fundamental to numerous environmental modelling and simulation analyses. We, therefore, developed the Geomorpho90m global dataset comprising of different geomorphometric features derived from the MERIT-Digital Elevation Model (DEM) - the best global, high-resolution DEM available. The fully-standardised 26 geomorphometric variables consist of layers that describe the (i) rate of change across the elevation gradient, using first and second derivatives, (ii) ruggedness, and (iii) geomorphological forms. The Geomorpho90m variables are available at 3 (~90 m) and 7.5 arc-second (~250 m) resolutions under the WGS84 geodetic datum, and 100 m spatial resolution under the Equi7 projection. They are useful for modelling applications in fields such as geomorphology, geology, hydrology, ecology and biogeography. Publication https://www.nature.com/articles/s41597-020-0479-6
description: Geomorphometric layers (geomorphological forms) base on MERIT-DEM computed at 100 m under Equi7 projection and then re-sampled at 7.5 arc-second (~250m) under WGS84 geographic coordinate system. Higher resolution (~90m) available at https://portal.opentopography.org/dataspace/dataset?opentopoID=OTDS.012020.4326.1 Technical documentation available at http://www.spatial-ecology.net/dokuwiki/doku.php?id=topovar90m
DOI: 10.5281/zenodo.1808740
title: TweetsKB (Part 6, Nov 2017 - March 2018)

description: <strong>TweetsKB</strong><strong> </strong>is a public RDF corpus of anonymized data for a large collection of <strong>annotated </strong>tweets. The dataset currently contains data for more than <strong>1.9 billion tweets</strong>, spanning more than 7 years (2013 - 2020). Metadata information about the tweets as well as extracted <strong>entities</strong>, <strong>sentiments</strong>, <strong>hashtags</strong>, <strong>user mentions</strong> and <strong>URLs </strong>are exposed in RDF using established RDF/S vocabularies*. Example queries and more information are available through TweetsKB's home page: https://data.gesis.org/tweetskb/. <em>* For the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets.</em>
DOI: 10.5281/zenodo.1808741
title: TweetsKB (Part 6, Nov 2017 - March 2018)

description: <strong>TweetsKB</strong><strong> </strong>is a public RDF corpus of anonymized data for a large collection of <strong>annotated </strong>tweets. The dataset currently contains data for more than <strong>1.9 billion tweets</strong>, spanning more than 7 years (2013 - 2020). Metadata information about the tweets as well as extracted <strong>entities</strong>, <strong>sentiments</strong>, <strong>hashtags</strong>, <strong>user mentions</strong> and <strong>URLs </strong>are exposed in RDF using established RDF/S vocabularies*. Example queries and more information are available through TweetsKB's home page: https://data.gesis.org/tweetskb/. <em>* For the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets.</em>
DOI: 10.5281/zenodo.1814337
title: A field guide for the compositional analysis of any-omics data: Supplemental Scripts

description: Supplemental Scripts for "A field guide for the compositional analysis of any-omics data".
DOI: 10.14469/hpc/4831
title: 3Br2 + 2,4,6-tributylbenzene in CCl4, Reactant I (alt) ÎG = -16149.136153

description: Gaussian Calculation
DOI: 10.5281/zenodo.1461711
title: The fatigue damage evolution in the load-carrying composite laminates of wind turbine blades

description: Video and data-set behind the figures used in the book chapter: Mikkelsen, LP, The fatigue Damage evolution in the load-carrying composite laminates of wind turbine blades, chapter 16 in “Fatigue life prediction of composites and composite structures”, Edited by A.P. Vassilopoulos, Second edition, Elsevier Ltd, To be published in 2019. https://doi.org/10.1016/B978-0-08-102575-8.00016-4. The files are organised based on the first characters in the filename with fist two numbers and than a "a" for video and "b,c.." for data behind that video. The video have the following link to the figures in the book chapter: Figure 16.19: 03a-Eps05-2-Zoom.mp4 , https://youtu.be/PynQ0IEyne8<br> Figure 16.21A: 01a-2Dprojection.avi , https://youtu.be/sr1J_5oc2EE<br> Figure 16.21B: 02a-Video 3D 360.mpg , https://youtu.be/riqtI6fARko<br> Figure 16.25: 11a-Eps05-2.mp4 , https://youtu.be/RJwfpiiLws0<br> Figure 16.28A: 21a-LFoVexsitu.mp4 , https://youtu.be/PmUWAI9qfMQ<br> Figure 16.28B: 22a-SFoV1G.mp4 , https://youtu.be/sFBgVyF1Bfw<br> Figure 16.29: 23a-SFoVGreen.mp4 , https://youtu.be/wJMV9bP5ntw<br> Figure 16.30: 24a-SFoVRed.mp4 , https://youtu.be/9m1Q0MrzJrY<br> Figure 16.31: 25a-RotatingSegmentedDamage.mpg , https://youtu.be/OHPkwwaTUSM<br> Figure 16.34: 31a-Stained.mpg , https://youtu.be/Sddg0sbzw6w<br> Figure 16.35: 41a-RLmovie.mpg , https://youtu.be/H7aZrIYjKeQ<br> Figure 16.36A: 51a-SFoV-Tension-Compression-T.mpg , https://youtu.be/AdsjDkq8QE8<br> Figure 16.36B: 52a-SFoV-Tension-Compression-W.mpg , https://youtu.be/ddSqQnW8jMo
DOI: 10.7910/dvn/fuo7fc/9jeziy
title: ProprietaryDataGraphYEAR.png

description: Updated to include 2017
DOI: 10.5281/zenodo.1883079
title: TESS-NOVEMBER-2018

description: Measurements taken by the European Photometer Network (Project STARS4ALL). November 2018
DOI: 10.5281/zenodo.1883080
title: TESS-NOVEMBER-2018

description: Measurements taken by the European Photometer Network (Project STARS4ALL). November 2018
DOI: 10.5285/e2921d09-975c-4236-8acf-039d7462762e
title: CTD fluorescence and faecal pellet data from the Ecosystems Western Core Box and Moorings cruise to the Scotia Sea, Southern Ocean (JR304)

description: Zooplankton faecal pellet abundance, volume and flux were determined from samples collected at three stations in the Scotia Sea, Southern Ocean during cruise JR304. Samples were collected at six depths within the 0 - 400 m epi- to upper mesopelagic using Niskin bottles attached to a CTD unit and were preserved in a formalin-based solution. Fluorescence data were collected during the same deployments. Sampling was performed by C. Liszka and G. Tarling on board RRS James Clark Ross. Sample analysis was performed by C. Liszka at BAS HQ in Cambridge.
description: An SBE9Plus CTD was used to collect fluorescence data from three stations in the Scotia Sea, which was converted from volts into chl-a (µg l-1) using the manufacturer's calibration. Chl-a concentrations were used as a proxy for the relative primary productivity of each station. 12 litre Niskin bottles attached to a SBE11Plus deck unit collected water for faecal pellet analysis at the same stations. Six bottles were fired, at 5 m, 20 m, 40 m, 100 m, 200 m and 400 m. On deck, water was gently siphoned out of the Niskin bottles via a piece of silicone tubing, which had been pre-rinsed three times with 0.22 µm filtered seawater (FSW), into pre-rinsed 20 L carboys. To ensure collection of all possible faecal material from the funnelled base of the Niskin bottles, the base of each bottle was opened, rinsed with FSW, and the water collected in separate sterile 250 ml Nalgene bottles. The contents of the carboys and Nalgene bottles were then gently filtered through a 53 µm mesh and bottles rinsed through with FSW. The contents of the mesh were backwashed into a 250 ml sterile Nalgene bottle using a 5% borax-buffered formalin-seawater solution. For sample analysis, the contents of Nalgene bottles were filtered onto a 53 µm mesh, gently rinsed and backwashed into petri dishes. Petri dishes were examined under a light microscope (Olympus SZX16 with SDF PLAPO 0.5XPF and 1.6XPF objectives) for whole or fragmented faecal pellets and other organic matter (e.g. diatoms, protozoa and marine snow). FPs were photographed using a Canon EOS 70D camera. FPs were categorised into morphological type and the length (L) and width (W) were measured with imaging software, Image J (Rasband, 1997-2016). FP abundance m-3 was calculated by dividing abundance per sample by the volume of the Niskin sampling bottle (12 L) and multiplying by 1000. The same calculation was used to calculate the volume and carbon of FPs m-3.
description: To support identification, some items that were difficult to classify using the light microscope alone were extracted and analysed using scanning electron microscopy.
DOI: 10.5066/p9js1k1y
title: Supplemental Discrete Dissolved-Solids Data and Monthly Mean Dissolved-Solids Data for Lower Colorado River (1928-2016)

description: Monthly Mean Dissolved-Solids Concentration and Monthly Mean Dissolved-Solids Load Data Flow weighted monthly mean dissolved-solids concentrations (mg/L) data and monthly mean dissolved-solids load data from 1928-2016 were computed by USGS using raw data from the Bureau of Reclamation. These data were computed by USGS for all of the seven sites (listed below). Colorado River above Imperial Dam, AZ-CA, 09429490 (1976-2016) Colorado River at Lees Ferry, AZ, 09380000 (1947-2016) Colorado River at Northern International Boundary, above Morelos Dam, AZ, 09522000 (1961-2016) Colorado River below Hoover Dam, AZ-NV, 09421500 (1948-2016) Colorado River near Cisco, UT, 09180500 (1928-2016) Green River at Green River, UT, 09315000 (1928-2016) San Juan River near Bluff, UT, 09379500 (1929-2016) Monthly mean dissolved-solids concentrations and loads were not calculated for several time periods (listed below) because of insufficient discrete dissolved-solids concentration data: Colorado River below Hoover Dam, AZ-NV, 09421500 (October 1962 - September 1953) Colorado River near Cisco, UT, 09180500 (October 1936 - September 1938 and October 1939 - September 1940) Green River at Green River, UT, 09315000 (October 1936 - September 1938, October 1939 - September 1940, and October 1942 - September 1943) Discrete Dissolved-Solids Concentration Data Discrete dissolved-solids concentrations (mg/L) data and specific conductance (microsiemens/cm) from 1990-2016 were computed using raw data from the Bureau of Reclamation. These data were computed for four sites (listed below). Colorado River above Imperial Dam, AZ-CA, 09429490 (1990-2016), dissolved-solids Colorado River above Imperial Dam, AZ-CA, 09429490 (1993-2016), specific conductance Colorado River below Hoover Dam, AZ-NV, 09421500 (1993-2016), dissolved-solids Colorado River at Northern International Boundary, above Morelos Dam, AZ, 09522000 (2001-2016), dissolved-solids
DOI: 10.5066/p9x3c9hl
title: Chemistry data for assessment of the containment pond at Canyon Mine, Arizona 2017

description: These data are comprised of measurements of elements (e.g., uranium, cobalt, nickel, copper, zinc, cadmium, lead, etc.), major anions (chloride, nitrite+nitrate as nitrogen, sulfate, etc.), dissolved organic carbon, and general water quality characteristics in Canyon Mine containment pond water, sediment, vegetation and invertebrate samples collected in calendar year 2017.
DOI: 10.18739/a2cn6xz7h
title: Paleoecological and paleoenvironmental proxy data from Toolik Lake, Lake E5, Lake Fog 2, and Upper Capsule Lake, North Slope, Alaska, 2014-2018
DOI: 10.21223/p3/cblcq4
title: Dataset for: Multilocation phenotyping of the 'Bearegard by Tanzania' mapping populations in Uganda

description: The BT population is a bi-parental cross comprising 317 F1 genotypes that were evaluated along with the two parents (‘Beauregard’ and ‘Tanzania’) and one international check clone ‘CIP1999062.1’. The trial was conducted at three locations in Uganda (Namulonge, Serere and Kachwekano) for two seasons 2016 B and 2017 A in an alpha lattice experimental design. Each genotype was planted with 16 plants per plot which were replicated three times per site. Data for every season at each location was checked for consistency using the command spconsis of package st4gi in R. Any value that was three times the interquartile range was detected as an outlier. Standardized residuals for each observation were calculated using the Proc GLM and the Proc Univariate procedures in SAS. Genotype observations with residuals ± 4 were set to missing values.
DOI: 10.21223/p3/gjday3
title: Dataset for: Orange fleshed Observation Trials at Gurue Research Station evaluated in 2017

description: The Observation Trial was carried out in 2017 season B at Gurue Research Station. The trial had 2667 experimental clones, 2 check clones and progenitors/parents of the experimental clones. Trial design was Westcott. Each clone was planted to a single row plot, 1 m long, replicated two times following a randomized complete block design. The check clones were alternatively planted after every 10 experimental clones. The objective of the trial was to select clones with orange flesh, tolerant to weevils, virus, high yielding, high plant vigour and high dry matter. The clones meeting the above mentioned characters were advanced to preliminary yield trials. Raw data for commercial root yield, non-commercial root yield and vine yield are presented in the report.
DOI: 10.21223/p3/z73pfp
title: Dataset for: Multi-location confined field trials (ML-CFTs) for dossier development of biotech late blight resistant potato in Uganda

description: Transgenic potato Vic.1 carries three resistance (R) genes from wild potato relatives that were introduced to confer resistance against potato late blight (LB) disease caused by Phytophthora infestans. Preliminary field trials show that the introduction of these R genes indeed confers extreme resistance to P. infestans. In this regulatory studies, we aim to confirm the effectiveness of the introduction of the R genes in Vic.1 by conducting confined field trials during two seasons at three locations.
DOI: 10.5063/f16m352m
title: Elevation per SASAP region and Hydrolic Unit (HUC8) boundary for Alaskan watersheds

description: This dataset was created to assess regions and watersheds of Alaska for mean elevation, minimum elevation, maximum elevation, median elevation, standard deviation of elevation, range of elevation and coefficient of variation of elevation in each SASAP region and each HUC8 watershed of Alaska. Three DEM's were mosaicked to make an Alaska-wide tiff. These include separate files for Alaska, the Yukon, and British Columbia. They were combined with the "sasap_regions.zip" shapefile (located here: https://knb.ecoinformatics.org/#view/urn:uuid:2c1c26fc-bfb9-4b6a-8d4a-0be8e61c4deb) to create the shapefile, "regions_elevation_shp.zip" and with the "sasap_watersheds_gapfix.zip" shapefile (located here: https://knb.ecoinformatics.org/#view/urn:uuid:2b5ab57e-38ec-4bc9-8290-f080ec0befb4) to create the shapefile "watersheds_elevation_shp.zip". CSV versions of the resulting shapefiles are also archived. The included python script, which was used to merge the data, outlines the process in more detail.
DOI: 10.5063/f1bc3wsj
title: Single Species Conservation as an Umbrella for Management of Landscape Threats

description: Single species conservation unites disparate partners for the conservation of one species. However, there are widespread concerns that single species conservation biases conservation efforts towards charismatic species at the expense of others. Here we investigate the extent to which sage grouse (Centrocercus sp.) conservation, the largest public-private conservation effort for a single species in the US, provides protections for other species from localized and landscape-scale threats. We compared the coverage provided by sage grouse Priority Areas for Conservation (PACs) to 81 sagebrush-associated vertebrate species distributions with potential coverage under multi-species conservation prioritization generated using the decision support tool Zonation. PACs. We found that the current PAC prioritization approach was not statistically different from a diversity-based prioritization approach and covers 23.3% of the landscape, and 24.8%, on average, of the habitat of the 81 species. The proportion of each species distribution at risk was lower inside PACs as compared to the region as a whole, even without management (land use change 30% lower, cheatgrass invasion 19% lower). Whether or not bias away from threat represents the most efficient use of conservation effort is a matter of considerable debate, though may be pragmatic in this landscape where capacity to address these threats is limited. The approach outlined here can be used to evaluate biological equitability of protections provided by flagship species in other settings.
DOI: 10.14469/hpc/4832
title: Test Dataset for Imperial Data Repository

description: Test deposit
DOI: 10.14469/hpc/4833
title: 4Br2 + 2,4,6-tributylbenzene in CCl4, Product GD3BJ Def2-TZVPP freq -21297.510783

description: Gaussian Calculation
DOI: 10.5063/f12v2dcb
title: Elevation per SASAP region and Hydrolic Unit (HUC8) boundary for Alaskan watersheds

description: This dataset was created to assess regions and watersheds of Alaska for mean elevation, minimum elevation, maximum elevation, median elevation, standard deviation of elevation, range of elevation and coefficient of variation of elevation in each SASAP region and each HUC8 watershed of Alaska. Three DEM's were mosaicked to make an Alaska-wide tiff. These include separate files for Alaska, the Yukon, and British Columbia. They were combined with the "sasap_regions.zip" shapefile (located here: https://knb.ecoinformatics.org/#view/urn:uuid:2c1c26fc-bfb9-4b6a-8d4a-0be8e61c4deb) to create the shapefile, "regions_elevation_shp.zip" and with the "sasap_watersheds_gapfix.zip" shapefile (located here: https://knb.ecoinformatics.org/#view/urn:uuid:2b5ab57e-38ec-4bc9-8290-f080ec0befb4) to create the shapefile "watersheds_elevation_shp.zip". CSV versions of the resulting shapefiles are also archived. The included python script, which was used to merge the data, outlines the process in more detail.
DOI: 10.14469/hpc/4834
title: 5Br2 + 2,4,6-tributylbenzene in CCl4, Reactant I, ÎG = -26445.899358

description: Gaussian Calculation