cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6073/pasta/13ea8f578654493155a660ab886f695e
title: North Temperate Lakes LTER: Phytoplankton - Madison Lakes Area 1995 - current

description: Phytoplankton samples for the 4 southern Wisconsin LTER lakes (Mendota, Monona, Wingra, Fish) have been collected for analysis by LTER since 1995 (1996 Wingra, Fish) when the southern Wisconsin lakes were added to the North Temperate Lakes LTER project. Samples are collected as a composite whole-water sample and are preserved in gluteraldehyde. Composite sample depths are 0-8 meters for Lake Mendota (to conform to samples collected and analyzed since 1990 for a UW/DNR food web research study), and 0-2 meters for the other three lakes. A tube sampler is used for the 0-8 m Lake Mendota samples; samples for the other lakes are obtained by collecting water at 1-meter intervals using a Kemmerer water sampler and compositing the samples in a bucket. Samples are taken in the deep hole region of each lake at the same time and location as other limnological sampling. Phytoplankton samples are analyzed by PhycoTech, Inc., a private lab specializing in phytoplankton analyses (see data protocol for procedures). Samples for Wingra and Fish lakes are archived but not routinely counted. Permanent slide mounts (3 per sample) are prepared for all analyzed Mendota and Monona samples as well as 6 samples per year for Wingra and Fish; the slide mounts are archived at the University of Wisconsin - Madison Zoology Museum. Phytoplankton are identified to species using an inverted microscope (Utermohl technique) and are reported as natural unit (i.e., colonies, filaments, or single cells) densities per mL, cell densities per mL, and algal biovolume densities per mL. Multiple entries for the same species on the same date may be due to different variants or vegetative states - (e.g., colonial or attached vs. free cell.) Biovolumes for individual cells of each species are determined during the counting procedure by obtaining cell measurements needed to calculate volumes for geometric solids (e.g., cylinders, spheres, truncated cones) corresponding to actual cell shapes. Biovolume concentrations are then computed by mulitplying the average cell biovolume by the cell densities in the water sample. Note that one million cubicMicrometers of biovolume PerMilliliter of water are equal to a biovolume concentration of one cubicMillimeterPerMilliliter. Assuming a cell density equal to water, a cubicMillimeterPerMilliliter of biovolume converts to a biomass concentration of one milligramPerLiter. Sampling Frequency: bi-weekly during ice-free season from late March or early April through early September, then every 4 weeks through late November; sampling is conducted usually once during the winter (depending on ice conditions). Number of sites: 4 Several taxonomic updates have been made to this dataset February 2013, see methods for details.
DOI: 10.5066/p94w41el
title: MODFLOW-OWHM model used to simulate groundwater flow and evaluate storage in the Lucerne Valley Groundwater Basin, California

description: A three-dimensional groundwater flow model, MODFLOW-OWHM, was developed to provide a better understanding of the hydrogeology of the Lucerne Valley Groundwater Basin, California. The model was used to investigate the historical groundwater storage loss and subsidence associated with anthropogenic groundwater demands. The model was calibrated to 1942 through 2016 conditions. This USGS data release contains all of the input and output files for the simulation described in the associated model documentation report https://doi.org/10.3133/sir20225048
DOI: 10.22008/fk2/rtsydd
title: North-west Greenland ice mask

description: North-west Greenland ice mask.
DOI: 10.5281/zenodo.6782889
title: The Good First Issue Recommendation Dataset from "GFI-Bot: Automated Good First Issue Recommendation on GitHub"

description: This is a good first issue (GFI) recommendation dataset created from the GFI-Bot project (https://github.com/osslab-pku/gfi-bot). For more information about the GFI recommendation problem and GFI-Bot, please check our publications: Wenxin Xiao, Hao He, Weiwei Xu, Xin Tan, Jinhao Dong, and Minghui Zhou. 2022. Recommending Good First Issues in GitHub OSS Projects. In Proceedings of the 44th International Conference on Software Engineering, ICSE 2022, Pittsburgh, PA, USA, May 21–29, 2022. ACM. https://hehao98.github.io/files/2022-recgfi.pdf Hao He, Haonan Su, Wenxin Xiao, Runzhi He, and Minghui Zhou. 2022. GFI-Bot: Automated Good First Issue Recommendation on GitHub. Currently Under Review at the ESEC/FSE 2022 Demonstration Track. https://hehao98.github.io/files/2022-gfibot.pdf The dataset is a MongoDB dump and needs to be restored to a MongoDB instance before use. This can be done via the official <code>mongorestore</code> tool by running a command like this in the <code>dataset/</code> folder: <pre><code class="language-bash">mongorestore --uri={{ your mongodb url }} --gzip </code></pre> In the <code>gfibot.dataset</code> collection, each document describes the state of an issue at a certain time (either at the time of issue creation or at the time of issue resolution). The <code>resolver_commit_num</code> is the ground truth label (i.e., # of commits the issue resolver has made in the repository before issue resolution, excluding commits for resolving the issue itself; <code>resolver_commit_num = 0</code> means the resolver is someone completely new to the repository). The remaining fields can be used as features or further analyzed to derive new features. This dataset can be used to evaluate new GFI recommendation approaches. We hope it will be helpful in advancing GFI recommendation research and other future studies on open-source software onboarding.
DOI: 10.5281/zenodo.6783526
title: Hieraaetus morphnoides decontaminated gx

description: .
DOI: 10.5281/zenodo.6783525
title: Hieraaetus morphnoides decontaminated gx

description: .
DOI: 10.5066/p9j11vv6
title: Topobathymetric Model of the Coastal Georgia, 1851 to 2020

description: To support Hurricane Florence impact modeling of storm-induced flooding and sediment transport, the U.S. Geological Survey (USGS) Coastal National Elevation Database (CoNED) Applications Project has created an integrated 1-meter topobathymetric digital elevation model (TBDEM) for coastal Georgia. High-resolution coastal topobathymetric data are required to characterize flooding, storms, and sea-level rise inundation hazard zones and other earth science applications, such as the development of sediment transport and storm surge models. This TBDEM consists of the best available multi-source topographic and bathymetric elevation data for Coastal Georgia including neighboring bays, estuaries, waterways, inlets, and islands. The Coastal Georgia TBDEM integrates 16 different data sources including topographic and bathymetric data, such as lidar point clouds and multi-beam acoustic surveys obtained USGS, the National Oceanic and Atmospheric Administration, the U.S. Army Corps of Engineers, and Federal Emergency Management Agency. The topographic and bathymetric surveys were sorted and prioritized based on survey date, accuracy, spatial distribution, and point density to develop a model based on the best available elevation and bathymetric data. Because bathymetric data are typically referenced to tidal datums, such as Mean High Water or Mean Low Water, all tidally referenced heights were transformed into orthometric heights based on the GEOID12B geoid, which is normally used for mapping elevation on land using the North American Vertical Datum of 1988. The spatial horizontal resolution is 1-meter with the general location ranging from the South Carolina border to the Georgia/Florida border. The overall temporal range of the input topography and bathymetry is 1851 to 2020 with a maximum depth extending to 44.4 meters. The topography surveys are from 1999-2020. The bathymetry surveys were acquired between 1851 and 2020.This data release was funded by the USGS Coastal and Marine Hazards and Resources Program (CMHRP) for coastal Georgia.
DOI: 10.6084/m9.figshare.20200097
title: Data for neck abscess

description: The dataset is for the different cause of neck abscess.
DOI: 10.6084/m9.figshare.20200097.v1
title: Data for neck abscess

description: The dataset is for the different cause of neck abscess.
DOI: 10.5281/zenodo.6783568
title: Combinational Circuits Dataset

description: <strong>Dataset</strong> The dataset with the generated combinational circuits has a size of 100 thousand records with the following parameters: the number of inputs and outputs does not exceed 20; number of logical elements in each circuit does not exceed 1 thousand; number of circuits generated by the CCGRTT method: 20 thousand; number of circuits generated by the CCGRCG method: 20 thousand; number of circuits generated by the CCGRVC method: 20 thousand; number of circuits generated by the CCGGA method: 40 thousand. Each combinational circuit in the dataset is represented by the following parameters: numInputs - number of circuit inputs; numOutputs - number of circuit outputs; maxLevel - length of the critical path; enumeration of logical elements by types (const, and, nand, or, nor, not, xor, xnor, buf); the number of edges by type of logical elements (from-to, for example, const-nand); reliability_percent - combinational circuit reliability, expressed as a percentage. The first line of each dataset file lists the names of the parameters separated by commas. Each subsequent line corresponds to a separate combinational circuit. The order of the schema parameters is indicated on the first line. The parameters themselves are separated by commas. <strong>Program for generating combinational circuits</strong> Program for generating combinational circuits in Verilog format and calculation of their main parameters. <strong>Examples</strong> Examples of using the dataset are located in Prediction.
DOI: 10.5281/zenodo.6470188
title: msd-errors - Data for AgCrSe2 figures

description: Data for the figures Results of the analysis of AgCrSe2 systems by kinisi at a series of temperatures.<br><br>Created using showyourwork from this GitHub repo.<br>
DOI: 10.5281/zenodo.6470187
title: msd-errors - Data for AgCrSe2 figures

description: Data for the figures Results of the analysis of AgCrSe2 systems by kinisi at a series of temperatures.<br><br>Created using showyourwork from this GitHub repo.<br>
DOI: 10.5281/zenodo.6470192
title: msd-errors - Random walk data

description: The data related to the random walk components of the msd-error work, including truly random and temporarily anti-correlated random walks at a series of system and step size. These have either be analysed numerically or analytically (using kinisi, WLS, or OLS).<br><br>Created using showyourwork from this GitHub repo.<br>
DOI: 10.6084/m9.figshare.19775650.v2
title: ChAdOx1 nCoV-19 (AZD1222) or ChAdOx1 nCoV-19-Beta (AZD2816) protect Syrian hamsters against the Beta, Delta, and Omicron variants of concern

description: Raw data accompanying ' <strong>ChAdOx1 nCoV-19 (AZD1222) or ChAdOx1 nCoV-19-Beta (AZD2816) protect Syrian hamsters against the Beta, Delta, and Omicron variants of concern'</strong>
DOI: 10.6084/m9.figshare.20199557.v1
title: Figure 3-5.pzfx

description: <strong>Figure 3 The neutralization antibody titer against Omicron before and after booster administration.</strong> <strong>Figure 4 Cross-neutralization analysis of Spike protein-elicited sera before and after Beta/Omicron Spike booster administration. </strong> <strong>Figure 5 Classification of SARS-CoV-2 spike protein variants by principal component and correlation coefficient analysis. </strong>
DOI: 10.6084/m9.figshare.20199557
title: Figure 3-5.pzfx

description: <strong>Figure 3 The neutralization antibody titer against Omicron before and after booster administration.</strong> <strong>Figure 4 Cross-neutralization analysis of Spike protein-elicited sera before and after Beta/Omicron Spike booster administration. </strong> <strong>Figure 5 Classification of SARS-CoV-2 spike protein variants by principal component and correlation coefficient analysis. </strong>
DOI: 10.18164/05c4fa07-2a1e-46fa-a98a-1dc64bb97326
title: Hamilton Harbour Water Quality Data
title: Données sur la qualité de l’eau dans le port de Hamilton

description: Hamilton Harbour is located at the west end of Lake Ontario. It was designated as one of 43 Areas of Concern in 1987 under the GLWQA where 11 beneficial uses of the harbour were deemed impaired. One of the primary recommendations of the Hamilton Harbour Remedial Action Plan was to decrease nutrient loads to Hamilton Harbour and establish water quality targets to decrease undesirable algae and improve water clarity. This long term data set consists of spatial and temporal water quality data collected in Hamilton Harbour since 1987 to measure ecological response of remedial efforts.
description: Le port de Hamilton sur trouve du côté ouest du lac Ontario. Il a été désigné l’un des 43 secteurs préoccupants en 1987 en vertu de l’AQEGL du fait que 11 des utilisations bénéfiques du port étaient réputées être altérées. L’une des principales recommandations du Plan d’assainissement du port de Hamilton était de réduire les charges en éléments nutritifs dans le port de Hamilton et d’établir des cibles en matière de qualité de l’eau pour diminuer la quantité d’algues indésirables et améliorer la clarté de l’eau. Ces données à long terme comprennent des données spatiales et temporelles sur la qualité de l’eau recueillies dans le port de Hamilton depuis 1987 afin de mesurer la réponse écologique des efforts d’assainissement.
DOI: 10.18164/0704c4c4-8ffe-4be2-8d9b-a2f95b8f8075
title: Source Emissions, Oil Sands Region
title: Sources d'émissions, région des sables bitumineux

description: This Joint Oil Sands Monitoring Air Emissions Inventory (Inventory) for the oil sands region is part of the Joint Oil Sands Monitoring Program Emissions Inventory Compilation Report (Report). The Inventory is based on a synthesis of available information from several existing inventories and related emissions data sources compiled up to October 31, 2014.  The Inventory also includes input files for the Sparse Matrix Operator Kernel Emissions (SMOKE) emissions processing system, in order to allow users to create gridded input emissions files for air quality modelling.  This Inventory was developed to enhance the quality of Environment and Climate Change Canada’s high resolution forecasts and simulations of air quality in the oil sands region.  More information on the Inventory is contained in the Compilation Report including the data sources, compilation procedures, gap analysis, how some priority data gaps were addressed and recommendations for addressing other gaps.
description: Le présent Inventaire des émissions du programme de surveillance conjoint des sables bitumineux (l’Inventaire) pour la région des sables bitumineux fait partie du Rapport de compilation sur l’inventaire des émissions du Programme de surveillance conjoint des sables bitumineux (le Rapport). L’Inventaire repose sur une synthèse des données disponibles découlant de plusieurs inventaires d’émissions existants et de sources connexes compilés jusqu’au 31 octobre 2014. Il inclut également des fichiers de données pour le système de traitement des émissions SMOKE (Sparse Matrix Operator Kernel Emissions) afin que les utilisateurs puissent créer des fichiers de données maillées sur les émissions aux fins de la modélisation de la qualité de l’air. L’Inventaire a été conçu dans le but d’améliorer la qualité des prévisions et des simulations à haute résolution faites par Environnement et Changement climatique Canada de la qualité de l’air dans la région des sables bitumineux. Le Rapport offre un complément d’information sur l’Inventaire, y compris les sources des données, les procédures de compilation, l’analyse des lacunes, la prise en charge de certaines des lacunes les plus importantes et les recommandations concernant les autres lacunes
DOI: 10.25919/re8r-wz58
title: M13 PacBio amplicon sequencing of lichen herbarium specimens

description: M13 PacBio amplicon sequencing of lichen herbarium specimens (demultiplexed bam files) for 2022 MycoKeys publication
DOI: 10.5255/ukda-sn-855143
title: Qualitative Interviews From the Biomodifying Technologies Project, 2017-2020

description: This data set comprises de-identified transcripts of semi structured qualitative interviews conducted as part of the ESRC funded project “Biomodifying technologies and experimental space: organisational and regulatory implications for the translation and valuation of health research”. The project was led by researchers based in the Centre for Health, Law and Emerging Technologies at the University of Oxford in partnership with colleagues at the Universities of Sussex and York. Biomodifying technologies are those which modify living biological tissue in novel and increasingly patient-orientated and customised ways. The case study biomodifying technologies in this project were: ‘gene-editing’ whose goal is to understand and remove disease-related mutations, the creation of induced pluripotent stem cells that can be controlled to create different types of tissue for cell therapy, and the emergence of 3D printing of biological material which aims to create novel structures for bodily repair and renewal. The project addressed the following research questions: 1) How can the experimental space which these technologies currently occupy be characterised? What impact will this have on translational health research and its likely trajectories? 2) What are the challenges and risks posed by these technologies for existing, legal, regulatory and governance regimes? How are development strategies being shaped by these regimes? 3) What is defined as the benefit or value of these technologies? Which groups benefit or are seen to benefit, and how is benefit and value assessed? How does consensus about their value build? The project team utilised a mixed methods approach for UK fieldwork combining documentary analysis of various literatures, including the academic and grey literatures, with qualitative semi-structured interviews with a range of key stakeholders in each of the fields being studied. These include scientists working in academic laboratories, representatives of SMEs, research agencies, regulators, and senior staff in important service organisations.
DOI: 10.5281/zenodo.6082649
title: Highland forest's environmental complexity drives landscape genomics and connectivity of the rodent Peromyscus melanotis

description: We evaluate how the environmental complexity of La Malinche volcano influences patterns of genomic variation in <em>Peromyscus melanotis</em>, across two mountain slopes and global and regional geographic scales.Using reduced representation genomic sequencing we estimated population genetic diversity, subdivisions and migration rates. Remote sensing data and drone image processing were used to characterize landscape variables. We evaluated their effect on connectivity, based on a landscape analyses framework, using resistance surfaces, circuit theory and omnidirectional connectivity. Our findings showed how the forest environmental complexity across different geographic scales drives dispersal, genomic structure and connectivity patterns in this rodent, where dirt roads and disturbed areas limit its connectivity, while exhibiting higher connectivity at the highest elevations where the forest is less disturbed. Notably, that our 3D variable of tree height was significant demonstrates the utility in incorporating 3D vegetation structure variables into landscape genetic analyses.
DOI: 10.5281/zenodo.6082648
title: Highland forest's environmental complexity drives landscape genomics and connectivity of the rodent Peromyscus melanotis

description: We evaluate how the environmental complexity of La Malinche volcano influences patterns of genomic variation in <em>Peromyscus melanotis</em>, across two mountain slopes and global and regional geographic scales.Using reduced representation genomic sequencing we estimated population genetic diversity, subdivisions and migration rates. Remote sensing data and drone image processing were used to characterize landscape variables. We evaluated their effect on connectivity, based on a landscape analyses framework, using resistance surfaces, circuit theory and omnidirectional connectivity. Our findings showed how the forest environmental complexity across different geographic scales drives dispersal, genomic structure and connectivity patterns in this rodent, where dirt roads and disturbed areas limit its connectivity, while exhibiting higher connectivity at the highest elevations where the forest is less disturbed. Notably, that our 3D variable of tree height was significant demonstrates the utility in incorporating 3D vegetation structure variables into landscape genetic analyses.
DOI: 10.5281/zenodo.6783583
title: Limnodromus scolopaceus decontaminated gx

description: .
DOI: 10.5281/zenodo.6783582
title: Limnodromus scolopaceus decontaminated gx

description: .
DOI: 10.18164/057d08c0-9252-4435-b49e-2d7f82f61615
title: Northern Contaminants Program - Air Quality Monitoring (NCP-AQM)
title: Programme de lutte contre les contaminants dans le Nord – Surveillance de la qualité de l’air (PLCN-SQA)

description: The atmospheric monitoring subcomponent of the Northern Contaminants Program (NCP) studies long-range transport of anthropogenic organic pollutants in air (AOPA), mercury, and chemicals of emerging arctic concern from around the globe. This subcomponent includes three NCP Core Monitoring Projects, namely “Northern Contaminants Air Monitoring: Organic Pollutant Measurements”, “Mercury Measurements at Alert and Little Fox Lake”, and “Passive Air Sampling Network for Organic Pollutants and Mercury”. Long-term air measurements of contaminants are collected continuously throughout the year. In addition, there are several passive air sampling stations across the Arctic. The data are used to assess the effectiveness of national and international chemical control initiatives. This program is associated with the Chemical Management Plan, the Arctic Council’s Arctic Monitoring and Assessment Programme (AMAP), the Stockholm Convention on POPs, the Convention on Long-Range Transboundary Air Pollution Aarhus Protocol on POPs, and the Minimata Convention on Mercury.
description: Le volet sur la surveillance atmosphérique du Programme de lutte contre les contaminants dans le Nord (PLCN) porte sur le transport à grande distance des polluants organiques anthropiques dans l’air (AOPA), du mercure et des substances chimiques préoccupantes dans l’air de l’Arctique provenant de toute la planète. Ce volet comprend trois grands projets de surveillance du PLCN, soit la Surveillance des contaminants atmosphériques dans le Nord : mesures des polluants organiques, les mesures du mercure à Alert et à Little Fox Lake, et le Réseau d’échantillonnage atmosphérique passif pour mesurer les polluants organiques et le mercure. Des mesures de contaminants atmosphériques prises sur une longue durée sont recueillies de façon continue tout au long de l’année. En outre, il y a plusieurs stations d’échantillonnage d’air passif dans l’Arctique. Les données servent à évaluer l’efficacité des initiatives nationales et internationales de lutte contre les produits chimiques. Ce programme est associé au Plan de gestion des produits chimiques, au Programme de surveillance et d’évaluation de l’Arctique (PSEA) du Conseil de l’Arctique, à la Convention de Stockholm sur les POP, à la Convention sur la pollution atmosphérique transfrontière à longue distance d’Aarhus sur les polluants organiques persistants et à la Convention de Minimata sur le mercure
DOI: 10.5281/zenodo.6783727
title: CBA Thinking Fadura

description: Data and calculation for the Cost Benefit Analysis (CBA) for Thinking Fadura Park. Deliverable 5.3: Cost-benefit analysis for four INHERIT case studies: Thinking Fadura.