cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6075/j0x34xn2
title: Observations of Solar Analog Field Stars from the Hamilton Echelle Spectrograph at Lick Observatory

description: To reduce the observations, we used the Hamilton spectrometer pipeline that is part of the XIDL package (https://github.com/profxj/xidl) The software requires both the science data and a number of calibration frames. The calibration frames include arc lamp spectra, flat field images that trace the orders, and "pixel flats." Specific details of the pipeline's application to this dataset can be obtained from the corresponding author.
description: This observing program used the Hamilton Echelle Spectrograph on the three meter Shane telescope at Lick Observatory to obtain high resolution spectroscopic observations of 211 solar analog field stars. Observations were made over 22 nights between February 2013 and January 2014. Our criteria for selecting solar analogs include: (1) effective temperature within ±500 K of solar; (2) -0.3 &lt; [Fe/H] &lt; +0.3; (3) absolute magnitude within ±0.5 of the Hipparcos main sequence (Wright 2004: AJ, 128, 1273). Effective temperatures were taken from the third version of the Geneva-Copenhagen survey (Holmberg et al. (2009: A&A, 501, 941), with temperature, metallicity and isochrone age estimates refined by Casagrande et al. (2011: A&A, A138). An additional selection criterion is that a star have no indication in the SIMBAD Astronomical Database of being part of a double or multiple system, or being a chromospherically active variable such as type RS CVn or BY Dra (note that SIMBAD entries are periodically updated and users should recheck this criterion). Table 1 (in the Readme) lists the program stars. Each ascii text file in this collection contains one spectrum, with the star's Hipparcos catalog identifier (HIP) as the filename. The columns in each file are, left to right: (1) Hamilton Echelle Spectrograph order number (integer); (2) Wavelength (Ångstroms); (3) Flux (machine units); (4) Variance (such that the signal-to-noise ratio and be estimated as the Flux divided by the square root of the Variance). Information about the Hamilton Echelle Spectrograph, including the table of orders, can be found at https://mthamilton.ucolick.org/techdocs/instruments/hamspec/index.html/
DOI: 10.5281/zenodo.8015187
title: Datasets for the Tiwary Lab contribution to the NIST JARVIS database

description: ala2.zip: test -- Alanine dipeptide in vacuum, simulated using GROMACS 2020.2 at 450 K using the amber99sb-ildn force field for 1 microsecond, unbiased ref -- Alanine dipeptide in vacuum, simulated using GROMACS 5.0 at 450 K using the amber03 force field for 800 ns, unbiased ala3.zip: test -- Alanine tetrapeptide in vacuum, simulated using GROMACS 2021.4 at 400 K using the amber99sb-ildn force field, three 1 microscond replicates, unbiased ref -- Alanine tetrapeptide in vacuum, simulated using GROMACS 5.0.4 at 400 K using the amber99sb-ildn force field for 1 microsecond, unbiased (trajectory data not included) aib9.zip: test -- Alpha-aminoisobutyric acid-9 solvated in TIP3P, simulated using GROMACS 2018.8 at 500 K using the CHARMM36m force field for 200 ns, unbiased ref -- Alpha-aminoisobutyric acid-9 solvated in TIP3P, simulated using GROMACS 2018.8 at 400 K using the CHARMM36m force field for 714 ns, biased using well-tempered metadynamics as described in https://pubs.acs.org/doi/10.1021/acs.jctc.2c00058 (trajectory data not included)
DOI: 10.5281/zenodo.8015186
title: Datasets for the Tiwary Lab contribution to the NIST JARVIS database

description: ala2.zip: test -- Alanine dipeptide in vacuum, simulated using GROMACS 2020.2 at 450 K using the amber99sb-ildn force field for 1 microsecond, unbiased ref -- Alanine dipeptide in vacuum, simulated using GROMACS 5.0 at 450 K using the amber03 force field for 800 ns, unbiased ala3.zip: test -- Alanine tetrapeptide in vacuum, simulated using GROMACS 2021.4 at 400 K using the amber99sb-ildn force field, three 1 microscond replicates, unbiased ref -- Alanine tetrapeptide in vacuum, simulated using GROMACS 5.0.4 at 400 K using the amber99sb-ildn force field for 1 microsecond, unbiased (trajectory data not included) aib9.zip: test -- Alpha-aminoisobutyric acid-9 solvated in TIP3P, simulated using GROMACS 2018.8 at 500 K using the CHARMM36m force field for 200 ns, unbiased ref -- Alpha-aminoisobutyric acid-9 solvated in TIP3P, simulated using GROMACS 2018.8 at 400 K using the CHARMM36m force field for 714 ns, biased using well-tempered metadynamics as described in https://pubs.acs.org/doi/10.1021/acs.jctc.2c00058 (trajectory data not included)
DOI: 10.5281/zenodo.8015056
title: Dataset of Dragonfly Sky Reduction Paper (reduced in Jan 2023)

description: The reduced images of the example dataset used for the Dragonfly sky reduction paper. The reduction pipeline was run in Jan 2023. The dataset contains G and R images of UW1787 and the G and R mosaics of Spider. The images are coadded using a median combination.
DOI: 10.5281/zenodo.8015055
title: Dataset of Dragonfly Sky Reduction Paper (reduced in Jan 2023)

description: The reduced images of the example dataset used for the Dragonfly sky reduction paper. The reduction pipeline was run in Jan 2023. The dataset contains G and R images of UW1787 and the G and R mosaics of Spider. The images are coadded using a median combination.
DOI: 10.5066/p9pyvv8b
title: Colorado River at Windy Gap near Granby, CO - 2022/06/13 Water Surface Velocity Map Using Particle Image Velocimetry

description: The dataset consists of a .csv and shapefile of measurements of surface velocity magnitude and direction at the Colorado River at Windy Gap near Granby, CO, on June 13, 2022. The dataset contains approximately 2 km of river length. The surface velocity measurements were made by applying Large-Scale Particle Image Velocimetry (LSPIV) techniques, using overlapping videos collected by small Unmanned Aircraft Systems (sUAS). Additional attributes, including divergence, curl, shear, and strain, were calculated from the surface velocity measurements and are included in the dataset. This data is ideal for instantaneous discharge, shear, strain, and divergence, but less so for eddies and mean reach values. This is because the frame rate of 30-frames-per-second more accurately profiles and averages instantaneous values seen during the capture interval as opposed to the stability of an eddy or representative mean values of a longer capture interval.
DOI: 10.5281/zenodo.8013071
title: German Renewable Energy Tariff Register

description: The TariffRegister compiles all tariff types paid to renewable energy units in Germany under the RES Act 2000 and its subsequent amendments. This dataset is based on the tariff files reported by the four German Transmission System Operators (TSOs) on their conjoint reporting webpage. It includes over 6500 different tariff types applicable from 2000 to 2023. The file consists of 19 descriptors, the tariff identification code (Tariff_ID), publication year, associated technology, commissioning year of applicable units, unit characterization criteria (Criteria1 and Criteria2), bonus details (Bonus_name and Bonus_start), fuel type (Fuel), tariff values (Tariff), management premium, "Ausfallvergütung" value, “Mieterstromzuschlag”, and date of tariff introduction or change. Additionally, two tariff categorization levels (Category1 and Category2) group tariffs into 29 and 17 respective categories, while the KWK descriptor indicates CHP-biomass plant subsidies and the "Sign" descriptor signifies positive or negative tariffs. Notes: The TariffRegister.csv can be matched with the PaymentRegister dataset to identify the payment types received by renewable energy units. The provided categorization process reflects the specific research needs of the Chair of Energy Economics at BTU and does not affect the original data, allowing users to choose whether to utilize the categorization descriptors. Users can update the dataset by following the outlined steps: Update the “Original files” by downloading the latest published data (here) Clean the files from any row that is not including a Tariff_ID Include the categorization descriptors by matching the categories based on the Tariff_ID and update the categories for new tariffs. Paste this information into a CSV file and store it under “Cleaned files” and check that the column names and data types match the information from previous files. Run the provided R code after modifying the top-level directory. This publication is part of a series of datasets on German Renewable Energy Sources Data, which also includes a UnitRegister, a PaymentRegister, and the combination of this information to analyze individual bids in renewable auctions. For a comprehensive understanding of the datasets and their relationships, we recommend referring to two related publications: Batz Liñeiro, T. B., Müsgens, F. (2023). "Evaluating the German onshore wind auction programme: An analysis based on individual bids." Energy Policy, 172, 113317. https://doi.org/10.1016/j.enpol.2022.113317 | Preprint Batz Liñeiro, T., Müsgens, F. (2021). "Evaluating the German PV auction program: The secrets of individual bids revealed." Energy Policy, 159, 112618. https://doi.org/10.1016/j.enpol.2021.112618 | Preprint
DOI: 10.5281/zenodo.8013070
title: German Renewable Energy Tariff Register

description: The TariffRegister compiles all tariff types paid to renewable energy units in Germany under the RES Act 2000 and its subsequent amendments. This dataset is based on the tariff files reported by the four German Transmission System Operators (TSOs) on their conjoint reporting webpage. It includes over 6500 different tariff types applicable from 2000 to 2023. The file consists of 19 descriptors, the tariff identification code (Tariff_ID), publication year, associated technology, commissioning year of applicable units, unit characterization criteria (Criteria1 and Criteria2), bonus details (Bonus_name and Bonus_start), fuel type (Fuel), tariff values (Tariff), management premium, "Ausfallvergütung" value, “Mieterstromzuschlag”, and date of tariff introduction or change. Additionally, two tariff categorization levels (Category1 and Category2) group tariffs into 29 and 17 respective categories, while the KWK descriptor indicates CHP-biomass plant subsidies and the "Sign" descriptor signifies positive or negative tariffs. Notes: The TariffRegister.csv can be matched with the PaymentRegister dataset to identify the payment types received by renewable energy units. The provided categorization process reflects the specific research needs of the Chair of Energy Economics at BTU and does not affect the original data, allowing users to choose whether to utilize the categorization descriptors. Users can update the dataset by following the outlined steps: Update the “Original files” by downloading the latest published data (here) Clean the files from any row that is not including a Tariff_ID Include the categorization descriptors by matching the categories based on the Tariff_ID and update the categories for new tariffs. Paste this information into a CSV file and store it under “Cleaned files” and check that the column names and data types match the information from previous files. Run the provided R code after modifying the top-level directory. This publication is part of a series of datasets on German Renewable Energy Sources Data, which also includes a UnitRegister, a PaymentRegister, and the combination of this information to analyze individual bids in renewable auctions. For a comprehensive understanding of the datasets and their relationships, we recommend referring to two related publications: Batz Liñeiro, T. B., Müsgens, F. (2023). "Evaluating the German onshore wind auction programme: An analysis based on individual bids." Energy Policy, 172, 113317. https://doi.org/10.1016/j.enpol.2022.113317 | Preprint Batz Liñeiro, T., Müsgens, F. (2021). "Evaluating the German PV auction program: The secrets of individual bids revealed." Energy Policy, 159, 112618. https://doi.org/10.1016/j.enpol.2021.112618 | Preprint
DOI: 10.15156/bio/sh1844380.08fu
title: SH1844380.08FU

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/sh1844381.08fu
title: SH1844381.08FU

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/sh475890.07fu
title: SH475890.07FU

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/sh475891.07fu
title: SH475891.07FU

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/sh475892.07fu
title: SH475892.07FU

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/sh1844382.08fu
title: SH1844382.08FU

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/sh1844383.08fu
title: SH1844383.08FU

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/sh1844384.08fu
title: SH1844384.08FU

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/sh475893.07fu
title: SH475893.07FU

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/sh475894.07fu
title: SH475894.07FU

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.