cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh3319161.08fu
title: SH3319161.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh3319162.08fu
title: SH3319162.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh3319163.08fu
title: SH3319163.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh3319164.08fu
title: SH3319164.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh3319165.08fu
title: SH3319165.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh3319166.08fu
title: SH3319166.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.6069/mn8d-mv45
title: SARS-CoV-2 Daily and Cumulative Infection Estimates 2019-2021

description: This dataset contains estimates of daily and cumulative infections with SARS-CoV-2 from the beginning of the pandemic through November 14, 2021, as well as estimates of cumulative COVID-19 deaths (adjusted for under-reporting) and estimates of the infection-detection ratio, infection-hospitalization ratio, and infection-fatality ratio. These are made available by day for 190 countries and territories – and subnational locations in 10 of those countries – aggregated into 21 regions and 7 super-regions and globally. Methods and limitations for the underlying models can be found in detail in the publication.
DOI: 10.25921/xbgh-qx60
title: NOAA/WDS Paleoclimatology - Paleo-pCO2 Database Global C3 Plant pCO2 Data over the Past 30 Thousand Years

description: This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Forcing. The data include parameters of climate forcing with a geographic location of Global. The time period coverage is from 30000 to -65 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.
DOI: 10.5281/zenodo.6425390
title: Research Data supporting "Pachyrhynchus weevils use 3D photonic crystals with varying degrees of order to create diverse and brilliant displays"

description: The research data is arranged into different folders containing the following files (.txt, .tif, .xlsx files; <em>italics</em>). This data and the descriptions below should be read in conjunction with the manuscript and “Supporting Info”, both of which may be found at the following DOI: 10.1002/smll.202200592.
description: This study was supported by the European Research Council (ERC) through grant PrISMoID (833895). The authors further acknowledge financial support by the Adolphe Merkle Foundation, the National Science Foundation (1856402), and the Swiss National Science Foundation (SNSF) through the National Center of Competence in Research \textit{Bio-Inspired Materials} and the Ambizione programme grant (168223).
DOI: 10.5281/zenodo.6425389
title: Research Data supporting "Pachyrhynchus weevils use 3D photonic crystals with varying degrees of order to create diverse and brilliant displays"

description: The research data is arranged into different folders containing the following files (.txt, .tif, .xlsx files; <em>italics</em>). This data and the descriptions below should be read in conjunction with the manuscript and “Supporting Info”, both of which may be found at the following DOI: 10.1002/smll.202200592.
description: This study was supported by the European Research Council (ERC) through grant PrISMoID (833895). The authors further acknowledge financial support by the Adolphe Merkle Foundation, the National Science Foundation (1856402), and the Swiss National Science Foundation (SNSF) through the National Center of Competence in Research \textit{Bio-Inspired Materials} and the Ambizione programme grant (168223).
DOI: 10.5066/p9pzlhyz
title: Daily streamflow performance benchmark defined by D-score (v0.1) for the National Hydrologic Model application of the Precipitation-Runoff Modeling System (v1 byObs Muskingum) at benchmark streamflow locations

description: This data release contains the D-score (version 0.1) daily streamflow performance benchmark results for the National Hydrologic Model Infrastructure application of the Precipitation-Runoff Modeling System (NHM) version 1 "byObs" calibration with Muskingum routing computed at streamflow benchmark locations (version 1) as defined by Foks and others (2022). Model error was determined by evaluating predicted daily mean streamflow versus observed daily mean streamflow. Using those errors, the D-score performance benchmark computes the mean squared logarithmic error (MSLE), then decomposes the overall MSLE into orthogonal components such as bias, distribution, and sequence (Hodson and others, 2021). For easier interpretation, the MSLE components can be passed through a scoring function as described in Hodson and others (2021). References: Foks, S.S., Towler, E., Hodson, T.O., Bock, A.R., Dickinson, J.E., Dugger, A.L., Dunne, K.A., Essaid, H.I., Miles, K.A., Over, T.M., Penn, C.A., Russell, A.M., Saxe, S.W., and Simeone, C.E., 2022, Streamflow benchmark locations for hydrologic model evaluation within the conterminous United States (cobalt gages): U.S. Geological Survey data release, https://doi.org/10.5066/P972P42Z. Hay, L.E., and LaFontaine, J.H., 2020, Application of the National Hydrologic Model Infrastructure with the Precipitation-Runoff Modeling System (NHM-PRMS), 1980-2016, Daymet Version 3 calibration: U.S. Geological Survey data release, https://doi.org/10.5066/P9PGZE0S. Hodson, T.O., Over, T.M., and Foks, S.S., 2021, Mean squared error, deconstructed. Journal of Advances in Modeling Earth Systems, v. 13, no. 12, e2021MS002681, https://doi.org/10.1029/2021MS002681.
DOI: 10.5066/p9mjdnrl
title: Daily streamflow performance benchmark defined by D-score (v0.1) for the National Water Model Retrospective (v2.1) at benchmark streamflow locations

description: This data release contains the D-score (version 0.1) daily streamflow performance benchmark results for the National Water Model (NWM) Retrospective version 2.1 computed at streamgage benchmark locations (version 1) as defined by Foks and others (2022). Model error was determined by evaluating predicted daily mean streamflow (aggregated from an hourly timestep) versus observed daily mean streamflow. Using those errors, the D-score performance benchmark computes the mean squared logarithmic error (MSLE), then decomposes the overall MSLE into orthogonal components such as bias, distribution, and sequence (Hodson and others, 2021). For easier interpretation, the MSLE components can be passed through a scoring function as described in Hodson and others (2021). References: Foks, S.S., Towler, E., Hodson, T.O., Bock, A.R., Dickinson, J.E., Dugger, A.L., Dunne, K.A., Essaid, H.I., Miles, K.A., Over, T.M., Penn, C.A., Russell, A.M., Saxe, S.W., and Simeone, C.E., 2022, Streamflow benchmark locations for hydrologic model evaluation within the conterminous United States (cobalt gages): U.S. Geological Survey data release, https://doi.org/10.5066/P972P42Z. Hodson, T.O., Over, T.M., and Foks, S.S., 2021, Mean squared error, deconstructed. Journal of Advances in Modeling Earth Systems, v. 13, no. 12, e2021MS002681, https://doi.org/10.1029/2021MS002681.
DOI: 10.13020/nw8b-mk79
title: Lake Superior moored temperature and currents, Spring 2015 to Spring 2021
DOI: 10.5281/zenodo.6463693
title: Google Scholar search record: ?start=0&q=lobster&hl=en&as_vis=0,5&as_sdt=0,5

description: File generated: Search date, time, timezone: 2022-04-15 18:54:14 (Europe/London) Search parameters: All these words: lobster None of these words: This exact word or phrase: "" Any these words: Language: en Between these years: and Number of pages exported: 1 Starting from page: 1 Citations included: TRUE Citations included: TRUE Search only in the title: FALSE Authors: Source: GS links generated: https://scholar.google.co.uk/scholar?start=0&q=lobster&hl=en&as_vis=0,5&as_sdt=0,5
DOI: 10.5281/zenodo.6463692
title: Google Scholar search record: ?start=0&q=lobster&hl=en&as_vis=0,5&as_sdt=0,5

description: File generated: Search date, time, timezone: 2022-04-15 18:54:14 (Europe/London) Search parameters: All these words: lobster None of these words: This exact word or phrase: "" Any these words: Language: en Between these years: and Number of pages exported: 1 Starting from page: 1 Citations included: TRUE Citations included: TRUE Search only in the title: FALSE Authors: Source: GS links generated: https://scholar.google.co.uk/scholar?start=0&q=lobster&hl=en&as_vis=0,5&as_sdt=0,5
DOI: 10.5066/p9glb4vb
title: Burn probability predictions for the state of California, USA using an optimal set of spatio-temporal features.

description: Burn probability (BP) models the likelihood that a location could burn. However, predicting BP is extremely challenging, because fire behavior varies strongly among landscapes and with changing weather conditions and wildfire spread simulations are computationally intensive and require integration of data with large spatial and temporal variability. In this data release we include the monthly BP estimation for the state of California, USA for the 2015-2019 period produced using a machine learning model and two different sets of input features. For the first case, the baseline, the model used all available input features to predict BP. The second output set corresponds to the BP predictions when the model used only the set of optimal features as determined in the cited paper.
DOI: 10.5281/zenodo.6463701
title: Google Scholar search record: ?start=0&q=lobster&hl=en&as_vis=0,5&as_sdt=0,5

description: File generated: Search date, time, timezone: 2022-04-15 18:57:53 (Europe/London) Search parameters: All these words: lobster None of these words: This exact word or phrase: "" Any these words: Language: en Between these years: and Number of pages exported: 1 Starting from page: 1 Citations included: TRUE Citations included: TRUE Search only in the title: FALSE Authors: Source: GS links generated: https://scholar.google.co.uk/scholar?start=0&q=lobster&hl=en&as_vis=0,5&as_sdt=0,5
DOI: 10.5281/zenodo.6463700
title: Google Scholar search record: ?start=0&q=lobster&hl=en&as_vis=0,5&as_sdt=0,5

description: File generated: Search date, time, timezone: 2022-04-15 18:57:53 (Europe/London) Search parameters: All these words: lobster None of these words: This exact word or phrase: "" Any these words: Language: en Between these years: and Number of pages exported: 1 Starting from page: 1 Citations included: TRUE Citations included: TRUE Search only in the title: FALSE Authors: Source: GS links generated: https://scholar.google.co.uk/scholar?start=0&q=lobster&hl=en&as_vis=0,5&as_sdt=0,5
DOI: 10.5281/zenodo.6463707
title: Google Scholar search record: ?start=0&q=lobster&hl=en&as_vis=0,5&as_sdt=0,5

description: File generated: Search date, time, timezone: 2022-04-15 18:59:13 (Europe/London) Search parameters: All these words: lobster None of these words: This exact word or phrase: "" Any these words: Language: en Between these years: and Number of pages exported: 1 Starting from page: 1 Citations included: TRUE Citations included: TRUE Search only in the title: FALSE Authors: Source: GS links generated: https://scholar.google.co.uk/scholar?start=0&q=lobster&hl=en&as_vis=0,5&as_sdt=0,5
DOI: 10.5281/zenodo.6463706
title: Google Scholar search record: ?start=0&q=lobster&hl=en&as_vis=0,5&as_sdt=0,5

description: File generated: Search date, time, timezone: 2022-04-15 18:59:13 (Europe/London) Search parameters: All these words: lobster None of these words: This exact word or phrase: "" Any these words: Language: en Between these years: and Number of pages exported: 1 Starting from page: 1 Citations included: TRUE Citations included: TRUE Search only in the title: FALSE Authors: Source: GS links generated: https://scholar.google.co.uk/scholar?start=0&q=lobster&hl=en&as_vis=0,5&as_sdt=0,5
DOI: 10.5281/zenodo.6457813
title: Inputs for lake morphology in ISIMIP3 runs of the global lake sector

description: Empirical evidence demonstrates that lakes and reservoirs are warming across the globe. Consequently, there is an increased need to project future changes in lake thermal structure and resulting changes in lake biogeochemistry in order to plan for the likely impacts. Previous studies of the impacts of climate change on lakes have often relied on a single model forced with limited scenario-driven projections of future climate for a relatively small number of lakes. As a result, our understanding of the effects of climate change on lakes is fragmentary, based on scattered studies using different data sources and modelling protocols, and mainly focused on individual lakes or lake regions. This has precluded identification of the main impacts of climate change on lakes at global and regional scales and has likely contributed to the lack of lake water quality considerations in policy-relevant documents, such as the Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC). The Lake Sector of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) was founded for simulating climate change impacts on lakes using an ensemble of lake models and climate change scenarios. The protocol prescribes lake simulations driven by climate forcing from gridded observations and different Earth system models under various Representative Greenhouse Gas Concentration Pathways, all consistently bias-corrected on a 0.5° × 0.5° global grid. The ISIMIP Lake Sector is the largest international effort to project future water temperature, thermal structure, and ice phenology of lakes at local and global scales and paves the way for future simulations of the impacts of climate change on water quality and biogeochemistry in lakes. For a comprehensive description of ISIMIP, sectors, and protocols please see https://www.isimip.org/ In order to simulate the impacts of climate change on lakes worldwide, the ISIMIP3 Lake Sector protocol has defined a set of lakes to be modelled by all participating lake models, as well as the basic morphometry (i.e., hypsographic curves) information for each lake. This repository includes all calculations performed to obtain the final set of lake location and mosphometry inpput information for ISIMIP3 runs. For this, available datasets on global lake extension and morphometry were used first for selecting a set of representative lakes on Earth (~40000 lakes, one for each 0.5º pixel of the normalized input/putput grid for ISIMIP across sectors), and then morphological characteristics were assigned to each representative lake using a database on global lake morphology. The final set of files constitute the input data for lake morphology and location for ISIMIP3 Lake Sector runs, which are produced in netCDF format.<br>
description: {"references": ["https://www.hydrosheds.org/pages/hydrolakes", "https://doi.org/10.1038/ncomms13603", "https://doi.org/10.6084/m9.figshare.c.5243309.v1", "https://doi.org/10.1038/s41597-022-01132-9"]}