cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14457/tu.the.2018.116
title: Corner point detection via walking particles

description: We propose a novel method for corner detection based on walking particles (WP) moving around the edge map of the image. The WP follows the edges and is intelligent to avoid the obstacles. The WP records its movement in the database to analyze and get more insights about the image. The main idea of the new method is that at the corner, the particle has less possible options to move, compared with the flat edge.  In other terms corner will limit the particles free movement. Lesser the freedom to move higher the chance to be a corner.To evaluate the WP method's success we performed two experiments. Firstly tested on a set of synthetic and real images against the most popular Harris (H) and Shi-Tomasi (ST) corner detection methods. In this synthetic image test, many distortion methods and added noise are introduced to the image to check the robustness of the algorithm. This experiment shows that the proposed method outperforms H and ST methods on 70% of distorted and normal images experiments and all the test cases with noise added images and real image of the evaluation. A Second experiment about retinal image registration and evaluated based on the root mean square error (RMSE). The result depicts that 81% of the test cases record the minimum RMSE by the proposed method. Therefore, the proposed method provides a novel approach to corner detection.
DOI: 10.5517/ccdc.csd.cc1z72fq
title: CCDC 1824053: Experimental Crystal Structure Determination

description: Related Article: Sanchari Dasgupta, Sourav Chatterjee, Tanmay Chattopadhyay|2019|J.Coord.Chem.|72|550|doi:10.1080/00958972.2019.1575963
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1zbj3y
title: CCDC 1827360: Experimental Crystal Structure Determination

description: Related Article: Han-Rui Tian, Miao-Miao Chen, Kai Wang, Zuo-Chang Chen, Chao-Yong Fu, Qianyan Zhang, Shu-Hui Li, Shun-Liu Deng, Yang-Rong Yao, Su-Yuan Xie, Rong-Bin Huang, Lan-Sun Zheng|2019|J.Am.Chem.Soc.|141|6651|doi:10.1021/jacs.9b01638
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.2598541
title: Supporting data for 'Key structural features, thermodynamics and kinetics along the pathway between active and inactive forms of thrombin'

description: This data set contains the raw data used for the preparation of the manuscript 'Key structural features, thermodynamics and kinetics along the pathway between active and inactive forms of thrombin'. It is separated into following parts:


	X-ray: Data used in the analysis of the PDB structures of thrombin, including a list of the PDB IDs, results of the PCA and values of the introduced features in the structures.
	cMD: Input parameters, topologies and starting coordinates of the five classical MD simulation that are based on different PDB structures, resulting trajectories, projection on X-ray PCA, RMSD values and values of the introduced features during the simulations.
	TMD: Input parameters, topologies and starting coordinates of the Targeted MD simulations, resulting trajectories, projection on X-ray PCA, RMSD values and values of the introduced features during the simulations.
	Seeded-Sims: Input parameters, topologies and starting coordinates of classical MD simulations that are started from cluster representatives of the TMD simulations and resulting trajectories.
	MSM: Values of the introduced features of the trajectories started from the TMD seeds, results of the TICA and of the denity-based clustering, representative structures of the states E, I1, I2 and E* and distribution of the introduced features in these states.


A more detailed description of the included files can be found in the <em>README</em> files in each folder. Additional information about software versions can also be found there.
DOI: 10.5281/zenodo.2598542
title: Supporting data for 'Key structural features, thermodynamics and kinetics along the pathway between active and inactive forms of thrombin'

description: This data set contains the raw data used for the preparation of the manuscript 'Key structural features, thermodynamics and kinetics along the pathway between active and inactive forms of thrombin'. It is separated into following parts:


	X-ray: Data used in the analysis of the PDB structures of thrombin, including a list of the PDB IDs, results of the PCA and values of the introduced features in the structures.
	cMD: Input parameters, topologies and starting coordinates of the five classical MD simulation that are based on different PDB structures, resulting trajectories, projection on X-ray PCA, RMSD values and values of the introduced features during the simulations.
	TMD: Input parameters, topologies and starting coordinates of the Targeted MD simulations, resulting trajectories, projection on X-ray PCA, RMSD values and values of the introduced features during the simulations.
	Seeded-Sims: Input parameters, topologies and starting coordinates of classical MD simulations that are started from cluster representatives of the TMD simulations and resulting trajectories.
	MSM: Values of the introduced features of the trajectories started from the TMD seeds, results of the TICA and of the denity-based clustering, representative structures of the states E, I1, I2 and E* and distribution of the introduced features in these states.


A more detailed description of the included files can be found in the <em>README</em> files in each folder. Additional information about software versions can also be found there.
DOI: 10.25919/5c9248b834b50
title: Parkes observations for project P958 semester 2018OCTS_61

description: A major breakthrough in fast radio burst (FRB) astronomy was recently made when the repeating FRB 121102 was localized to sub-arcsecond precision. The bursts were found to originate in a bright radio nebula (hypothesized to be a young supernova remnant or pulsar wind nebulae) in a distant dwarf galaxy. These dwarf galaxies are also preferential hosts to superluminous supernova and long gamma-ray bursts, and it has been therefore suggested that the source of the repeating FRB could be a young highly magnetised neutron star. While an understanding of the repeating FRB is emerging, it is still unclear what relationship it has to the rest of the population.   Over the last year searches with the Australian Square Kilometre Array Pathfinder (ASKAP) have detected 26 bursts, and over the next six month will likely detect another 12 FRBs, which  will be localized to a few arcseconds.   Here we propose to search this population for repeats.  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, will either confirm the presence of additional repeating FRBs or the uniqueness of FRB 121102 amongst the population.
DOI: 10.5063/f1sj1hwb
title: Salmon age, sex, and length data from Westward and Southeast Alaska, 1979-2017

description: Age, sex and length data provide population dynamics information that can indicate how populations trends occur and may be changing. These data can help researchers estimate population growth rates, age-class distribution and population demographics. Knowing population demographics, growth rates and trends is particularly valuable to fisheries managers who must perform population assessments to inform management decisions. These data are therefore particularly important in valuable fisheries like the salmon fisheries of Alaska. This dataset includes age, sex and length data compiled from annual sampling of commercial and subsistence salmon harvests and research projects in westward and southeast Kodiak. It includes data on five salmon species: chinook, chum, coho, pink and sockeye. Age estimates were made by examining scales or bony structures (e.g. otoliths - ear bones). Scales were removed from the side of the fish; usually the left side above the lateral line. Scales or bony structures were then mounted on gummed cards and pressed on acetate to make an impression. The number of freshwater and saltwater annuli (i.e. rings) was counted to estimate age in years. Age is recorded in European Notation, which is a method of recording both fresh and saltwater annuli. For example, for a fish that spent one year in freshwater and 3 years in saltwater, its age is recorded as 1.3. The total fish age is the sum of the first and second numbers, plus one to account for the time between deposition and emergence. Therefore the fish in this example is 5 years old. Fish sex was determined by either examining external morphology (eg. head and belly shape) or internal sex organ. Length was measured in millimeters, generally from mid-eye to the fork of the tail. This data package includes the original data file (ASL DATA EXPORT.csv), a reformatting script that reformats the original data file into a consistent format (ASL_Formatting_SoutheastKodiak.R), and the reformatted dataset as a .csv file (ASL_formatted_SoutheastKodiak.csv).
DOI: 10.5281/zenodo.2538061
title: Mulit-Criteria Power Line Routeing GIS_dataset 1

description: The dataset presented here contains GIS (Geographic Information System) data relevant for infrastructure projects, specifically development of DC grid.

The data has been sourced from a range of sources and proceeded to be compatible for combination and analysis. For each data set included, the conversion of the original data into a 500m grid in EPSG 31468 projection with the raster calculator implemented in QGIS has been carried out.


	River courses


Rivers are extracted from the Open Street Map database [1]. The search term waterway:river was used and extracted from the database using the Osmosis tool [2].


	Railroads


The data for the railway lines are extracted from the OSM database [1] using Osmosis and the search term railway:rails. However to exclude the tram lines in the urban areas the search term usage:main was used, to exclude non-heavily used lines.


	Highways


Gathered from OSM database [1] using Osmosis highway:roadway. The dataset includes all roads with multiple lanes and high traffic volume.


	Electricity grid


The electricity grid data set consists of high voltage power lines above 48kV from [1] (using osmosis and search term: power:lines and power:cable). The dataset contains all lines with extra-high voltage network and parts of the distribution network. For this dataset, all lines with low voltages have been excluded.

Acknowledgements:

Map data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org
description: {"references": ["[1]\u00a0OpenStreetMap contributors. Planet dump retrieved from https://planet.osm.org. https://www.openstreetmap.org, 2017", "[2] Osmosis.\u00a0 OpenStreetMap Wiki,  https://wiki.openstreetmap.org/wiki/Osmosis"]}
DOI: 10.5281/zenodo.2538062
title: Mulit-Criteria Power Line Routeing GIS_dataset 1

description: The dataset presented here contains GIS (Geographic Information System) data relevant for infrastructure projects, specifically development of DC grid.

The data has been sourced from a range of sources and proceeded to be compatible for combination and analysis. For each data set included, the conversion of the original data into a 500m grid in EPSG 31468 projection with the raster calculator implemented in QGIS has been carried out.


	River courses


Rivers are extracted from the Open Street Map database [1]. The search term waterway:river was used and extracted from the database using the Osmosis tool [2].


	Railroads


The data for the railway lines are extracted from the OSM database [1] using Osmosis and the search term railway:rails. However to exclude the tram lines in the urban areas the search term usage:main was used, to exclude non-heavily used lines.


	Highways


Gathered from OSM database [1] using Osmosis highway:roadway. The dataset includes all roads with multiple lanes and high traffic volume.


	Electricity grid


The electricity grid data set consists of high voltage power lines above 48kV from [1] (using osmosis and search term: power:lines and power:cable). The dataset contains all lines with extra-high voltage network and parts of the distribution network. For this dataset, all lines with low voltages have been excluded.

Acknowledgements:

Map data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org
description: {"references": ["[1]\u00a0OpenStreetMap contributors. Planet dump retrieved from https://planet.osm.org. https://www.openstreetmap.org, 2017", "[2] Osmosis.\u00a0 OpenStreetMap Wiki,  https://wiki.openstreetmap.org/wiki/Osmosis"]}
DOI: 10.5281/zenodo.2600025
title: Multi-angle light scattering (MALS) analysis of HTT1-3144 Q54 HAP401-371 samples purified from Sf9 insect cells - 2018/12/03

description: <strong>Project: </strong>Biophysical investigation of purified HTT protein samples

<strong>Experiment: </strong>Multi-angle light scattering (MALS) analysis of HTT<sup>1-3144 </sup>Q54 HAP40<sup>1-371</sup>samples purified from Sf9 insect cells

<strong>Date completed:­ </strong>2018/12/03

<strong>Rationale: </strong>Apo HTT<sup>1-3144 </sup>Q23 and HTT<sup>1-3144 </sup>Q23 HAP40<sup>1-371</sup>complex samples have been analysed by MALS revealing HTT-HAP40 complex samples to be monodisperse. PolyQ expanded HTT constructs have been expressed as HAP40 complexes but the monodispersity has not been assessed beyond analytical gel filtration.
description: Dr. Harding is the recipient of the Huntington's Disease Society of America Berman Topper Career Development Fellowship which funds and supports this research, in addition to generous funding from the CHDI Foundation and the Huntington Society of Canada. The SGC is a registered charity (number 1097737) that receives funds from AbbVie, Bayer Pharma AG, Boehringer Ingelheim, Canada Foundation for Innovation, Eshelman Institute for Innovation, Genome Canada through Ontario Genomics Institute [OGI-055], Innovative Medicines Initiative (EU/EFPIA) [ULTRA-DD grant no. 115766], Janssen, Merck KGaA, Darmstadt, Germany, MSD, Novartis Pharma AG, Ontario Ministry of Research, Innovation and Science (MRIS), Pfizer, São Paulo Research Foundation-FAPESP, Takeda, and Wellcome.
DOI: 10.5281/zenodo.2600026
title: Multi-angle light scattering (MALS) analysis of HTT1-3144 Q54 HAP401-371 samples purified from Sf9 insect cells - 2018/12/03

description: <strong>Project: </strong>Biophysical investigation of purified HTT protein samples

<strong>Experiment: </strong>Multi-angle light scattering (MALS) analysis of HTT<sup>1-3144 </sup>Q54 HAP40<sup>1-371</sup>samples purified from Sf9 insect cells

<strong>Date completed:­ </strong>2018/12/03

<strong>Rationale: </strong>Apo HTT<sup>1-3144 </sup>Q23 and HTT<sup>1-3144 </sup>Q23 HAP40<sup>1-371</sup>complex samples have been analysed by MALS revealing HTT-HAP40 complex samples to be monodisperse. PolyQ expanded HTT constructs have been expressed as HAP40 complexes but the monodispersity has not been assessed beyond analytical gel filtration.
description: Dr. Harding is the recipient of the Huntington's Disease Society of America Berman Topper Career Development Fellowship which funds and supports this research, in addition to generous funding from the CHDI Foundation and the Huntington Society of Canada. The SGC is a registered charity (number 1097737) that receives funds from AbbVie, Bayer Pharma AG, Boehringer Ingelheim, Canada Foundation for Innovation, Eshelman Institute for Innovation, Genome Canada through Ontario Genomics Institute [OGI-055], Innovative Medicines Initiative (EU/EFPIA) [ULTRA-DD grant no. 115766], Janssen, Merck KGaA, Darmstadt, Germany, MSD, Novartis Pharma AG, Ontario Ministry of Research, Innovation and Science (MRIS), Pfizer, São Paulo Research Foundation-FAPESP, Takeda, and Wellcome.
DOI: 10.17632/yfz98ybpgy.1
title: Data for: Households (un)willingness to perform private flood risk reduction - Results from a Swedish survey

description: Regression output
DOI: 10.5281/zenodo.2594685
title: Mulit-Criteria Power Line Routeing GIS_dataset 2

description: The dataset presented here contains GIS (Geographic Information System) data relevant for infrastructure projects, specifically development of DC grid.

The data has been sourced from a range of sources and proceeded to be compatible for combination and analysis. For each data set included, the conversion of the original data into a 500m grid in EPSG 31468 projection with the raster calculator implemented in QGIS has been carried out.


	Elevation


The elevation data originate from the EEA [1]. The resolution of the original data corresponds to about 25m and is available in raster format. The bilinear resampling method was used to determine the values of the new grid fields.


	Landscape quality assessment


Uniform assessment per grid field between 0 (low) and 10 (high) derived from the original data set of [2] with a total of information on 44 primary land use types based on the assessment criteria of [3]. The data set used here is the CLC2012 which refers to the reference year 2012. 


	Population density


Source of population density is a raster grid provided by the [4] which expresses the number of people per pixel with a resolution of 250m x 250m. The base year for population data is 2015.


	Protected areas


Reduction of data from [5] to continental Europe and change of projection.


	Slope


Derived from elevation data based on [1]. The slope is calculated by the tilt angle for each raster cell in degrees based on the first-order derivation.
description: {"references": ["[1] European Environment Agency.\u00a0 Digital Elevation Model over Europe (EU-DEM).\u00a0 Data retrieved from\u00a0https://www.eea.europa.eu/data-and-maps/data/copernicus-land-monitoring-service-eu-dem#tab-gis-data", "[2] European Environment Agency.\u00a0 Corine Land Cover 2012. Data retrieved from\u00a0https://land.copernicus.eu/pan-european/corine-land-cover", "[3] Br\u00e4m, R. Methode einer fl\u00e4chendeckenden Landschaftsbildbewertung der Schweiz basierend auf Sichtbarkeits- und Vektoranalysen im GIS. Technical report, Paris Lodron-Universit\u00e4t Salzburg, 2013", "[4] European Commission Joint Research Centre; Columbia University Center for International Earth Science Information476Network.\u00a0 GHS population grid, derived from GPW4, multitemporal (1975, 1990, 2000, 2015), 2018. The data retrieved from\u00a0http://data.jrc.ec.europa.eu/dataset/jrc-ghsl-ghs_pop_gpw4_globe_r2015a", "[5] IUCN and UNEP-WCMC. The World Database on Protected Areas (WDPA), 2018. Data retrieved from\u00a0https://www.protectedplanet.net/"]}
DOI: 10.3886/e100167v17-10902
title: National Surveys on Energy and Environment [United States]

description: The National Surveys on Energy and Environment (NSEE), a core activity in CLOSUP's Energy and Environmental Policy Initiative, reflects a formal partnership between the Muhlenberg Institute of Public Opinion at Muhlenberg College and the Center for Local, State, and Urban Policy at the University of Michigan's Gerald R. Ford School of Public Policy. NSEE surveys include twice per year national opinion surveys on issues directly related to climate change and energy policy, as well as other surveys conducted on a range of topics such as hydraulic fracturing ("fracking"), the Great Lakes, and wider issues of energy and environment. NSEE is co-directed by professor Barry Rabe at the University of Michigan, and professor Christopher Borick at Muhlenberg College. For more information on the collaboration between the University of Michigan and Muhlenberg College, please see the recent article from Muhlenberg Magazine. For more information about the NSEE, contact CLOSUP staff at 734-647-4091 or closup@umich.edu. From 2008-2012 the survey was called the “National Survey of American Public Opinion on Climate Change” (NSAPOOC); starting in 2013 the survey was renamed to the “National Surveys on Energy and Environment” (NSEE). <br><br>The NSEE is committed to transparency in all facets of our work, including timely release and posting of data from each survey wave. A grant from the Office of the Provost at the University of Michigan has allowed us to provide online access to earlier waves of the NSEE, including frequency tables, survey instruments, and datasets. Users can see a list of topics covered by the NSEE, and search for questions by text, variable name, or variable category on CLOSUP's website. <br><br>Although the datasets are listed by survey wave, the NSEE is a valuable source of longitudinal public-opinion data on climate change and energy policy. Many questions have been asked over multiple waves, including questions about belief in global warming that have been asked in every wave of the NSEE. Consult the NSEE Crosswalk to see which questions have been asked in prior and subsequent waves of the NSEE. To facilitate longitudinal analysis, the NSEE datasets use a longitudinal variable naming scheme to facilitate longitudinal analysis. Variable names include two parts: a subject category for the question, and a description of the contents of the question. When a question has been asked with the same text and response options over multiple waves, the same variable name will be used in each dataset. For more information on the longitudinal naming scheme users should consult the codebooks for the datasets. <br><br><br>