cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.1594/pangaea.961569
title: Seismic reflection profile AWI-20140324, Arctic Ocean

description: The data set provides unprocessed and processed seismic reflection data collected during Expedition PS87 (ARK-XXVIII/3) onboard RV Polarstern in the central and Arctic Ocean and Eurasia Basin in 2014. Detailed data acquisition parameters for the profile are documented in the expedition report (Stein, 2015). Metadata, recording parameters, and the seismic data processing sequence are documented in the EBCDIC-Headers enclosed in the seg-y files. Navigation information is included in the seg-y headers, and in the CDP-SHOT-latitude-longitude file (see data product). A ReadMe file additionally lists the metadata and recording parameters for the seismic profile. The data set is grouped into three categories: 1. Raw data comprise (a) demultiplexed seismic data in seg-y format, (b) a shot list of date, time and shot-number, and (c) navigation data with date, time, and coordinates. 2. Primary data consist of CDP-sorted data in seg-y format. 3. The data product is provided as (a) stacked or time-migrated version of the seismic profile in seg-y format and (b) a list with CDP and SHOT numbers as well as geographical coordinates. Please note that the low-frequency reflector that parallels the sea floor at depths of about 190 ms two-way travewltime (~150 m) is an artifact which has not been removed.
DOI: 10.1594/pangaea.961570
title: Seismic reflection profile AWI-20140318, Arctic Ocean

description: The data set provides unprocessed and processed seismic reflection data collected during Expedition PS87 (ARK-XXVIII/3) onboard RV Polarstern in the central and Arctic Ocean and Eurasia Basin in 2014. Detailed data acquisition parameters for the profile are documented in the expedition report (Stein, 2015). Metadata, recording parameters, and the seismic data processing sequence are documented in the EBCDIC-Headers enclosed in the seg-y files. Navigation information is included in the seg-y headers, and in the CDP-SHOT-latitude-longitude file (see data product). A ReadMe file additionally lists the metadata and recording parameters for the seismic profile. The data set is grouped into three categories: 1. Raw data comprise (a) demultiplexed seismic data in seg-y format, (b) a shot list of date, time and shot-number, and (c) navigation data with date, time, and coordinates. 2. Primary data consist of CDP-sorted data in seg-y format. 3. The data product is provided as (a) stacked or time-migrated version of the seismic profile in seg-y format and (b) a list with CDP and SHOT numbers as well as geographical coordinates. Please note that the low-frequency reflector that parallels the sea floor at depths of about 190 ms two-way travewltime (~150 m) is an artifact which has not been removed.
DOI: 10.1594/pangaea.961571
title: Seismic reflection profile AWI-20140321, Arctic Ocean

description: The data set provides unprocessed and processed seismic reflection data collected during Expedition PS87 (ARK-XXVIII/3) onboard RV Polarstern in the central and Arctic Ocean and Eurasia Basin in 2014. Detailed data acquisition parameters for the profile are documented in the expedition report (Stein, 2015). Metadata, recording parameters, and the seismic data processing sequence are documented in the EBCDIC-Headers enclosed in the seg-y files. Navigation information is included in the seg-y headers, and in the CDP-SHOT-latitude-longitude file (see data product). A ReadMe file additionally lists the metadata and recording parameters for the seismic profile. The data set is grouped into three categories: 1. Raw data comprise (a) demultiplexed seismic data in seg-y format, (b) a shot list of date, time and shot-number, and (c) navigation data with date, time, and coordinates. 2. Primary data consist of CDP-sorted data in seg-y format. 3. The data product is provided as (a) stacked or time-migrated version of the seismic profile in seg-y format and (b) a list with CDP and SHOT numbers as well as geographical coordinates. Please note that the low-frequency reflector that parallels the sea floor at depths of about 190 ms two-way travewltime (~150 m) is an artifact which has not been removed.
DOI: 10.1594/pangaea.961572
title: Seismic reflection profile AWI-20140327, Arctic Ocean

description: The data set provides unprocessed and processed seismic reflection data collected during Expedition PS87 (ARK-XXVIII/3) onboard RV Polarstern in the central and Arctic Ocean and Eurasia Basin in 2014. Detailed data acquisition parameters for the profile are documented in the expedition report (Stein, 2015). Metadata, recording parameters, and the seismic data processing sequence are documented in the EBCDIC-Headers enclosed in the seg-y files. Navigation information is included in the seg-y headers, and in the CDP-SHOT-latitude-longitude file (see data product). A ReadMe file additionally lists the metadata and recording parameters for the seismic profile. The data set is grouped into three categories: 1. Raw data comprise (a) demultiplexed seismic data in seg-y format, (b) a shot list of date, time and shot-number, and (c) navigation data with date, time, and coordinates. 2. Primary data consist of CDP-sorted data in seg-y format. 3. The data product is provided as (a) stacked or time-migrated version of the seismic profile in seg-y format and (b) a list with CDP and SHOT numbers as well as geographical coordinates. Please note that the low-frequency reflector that parallels the sea floor at depths of about 190 ms two-way travewltime (~150 m) is an artifact which has not been removed.
DOI: 10.1594/pangaea.961573
title: Seismic reflection profile AWI-20140322, Arctic Ocean

description: The data set provides unprocessed and processed seismic reflection data collected during Expedition PS87 (ARK-XXVIII/3) onboard RV Polarstern in the central and Arctic Ocean and Eurasia Basin in 2014. Detailed data acquisition parameters for the profile are documented in the expedition report (Stein, 2015). Metadata, recording parameters, and the seismic data processing sequence are documented in the EBCDIC-Headers enclosed in the seg-y files. Navigation information is included in the seg-y headers, and in the CDP-SHOT-latitude-longitude file (see data product). A ReadMe file additionally lists the metadata and recording parameters for the seismic profile. The data set is grouped into three categories: 1. Raw data comprise (a) demultiplexed seismic data in seg-y format, (b) a shot list of date, time and shot-number, and (c) navigation data with date, time, and coordinates. 2. Primary data consist of CDP-sorted data in seg-y format. 3. The data product is provided as (a) stacked or time-migrated version of the seismic profile in seg-y format and (b) a list with CDP and SHOT numbers as well as geographical coordinates. Please note that the low-frequency reflector that parallels the sea floor at depths of about 190 ms two-way travewltime (~150 m) is an artifact which has not been removed.
DOI: 10.1594/pangaea.961574
title: Seismic reflection profile AWI-20140326, Arctic Ocean

description: The data set provides unprocessed and processed seismic reflection data collected during Expedition PS87 (ARK-XXVIII/3) onboard RV Polarstern in the central and Arctic Ocean and Eurasia Basin in 2014. Detailed data acquisition parameters for the profile are documented in the expedition report (Stein, 2015). Metadata, recording parameters, and the seismic data processing sequence are documented in the EBCDIC-Headers enclosed in the seg-y files. Navigation information is included in the seg-y headers, and in the CDP-SHOT-latitude-longitude file (see data product). A ReadMe file additionally lists the metadata and recording parameters for the seismic profile. The data set is grouped into three categories: 1. Raw data comprise (a) demultiplexed seismic data in seg-y format, (b) a shot list of date, time and shot-number, and (c) navigation data with date, time, and coordinates. 2. Primary data consist of CDP-sorted data in seg-y format. 3. The data product is provided as (a) stacked or time-migrated version of the seismic profile in seg-y format and (b) a list with CDP and SHOT numbers as well as geographical coordinates. Please note that the low-frequency reflector that parallels the sea floor at depths of about 190 ms two-way travewltime (~150 m) is an artifact which has not been removed.
DOI: 10.1594/pangaea.961575
title: Seismic reflection profile AWI-20140323, Arctic Ocean

description: The data set provides unprocessed and processed seismic reflection data collected during Expedition PS87 (ARK-XXVIII/3) onboard RV Polarstern in the central and Arctic Ocean and Eurasia Basin in 2014. Detailed data acquisition parameters for the profile are documented in the expedition report (Stein, 2015). Metadata, recording parameters, and the seismic data processing sequence are documented in the EBCDIC-Headers enclosed in the seg-y files. Navigation information is included in the seg-y headers, and in the CDP-SHOT-latitude-longitude file (see data product). A ReadMe file additionally lists the metadata and recording parameters for the seismic profile. The data set is grouped into three categories: 1. Raw data comprise (a) demultiplexed seismic data in seg-y format, (b) a shot list of date, time and shot-number, and (c) navigation data with date, time, and coordinates. 2. Primary data consist of CDP-sorted data in seg-y format. 3. The data product is provided as (a) stacked or time-migrated version of the seismic profile in seg-y format and (b) a list with CDP and SHOT numbers as well as geographical coordinates. Please note that the low-frequency reflector that parallels the sea floor at depths of about 190 ms two-way travewltime (~150 m) is an artifact which has not been removed.
DOI: 10.1594/pangaea.961576
title: Seismic reflection profile AWI-20140319, Arctic Ocean

description: The data set provides unprocessed and processed seismic reflection data collected during Expedition PS87 (ARK-XXVIII/3) onboard RV Polarstern in the central and Arctic Ocean and Eurasia Basin in 2014. Detailed data acquisition parameters for the profile are documented in the expedition report (Stein, 2015). Metadata, recording parameters, and the seismic data processing sequence are documented in the EBCDIC-Headers enclosed in the seg-y files. Navigation information is included in the seg-y headers, and in the CDP-SHOT-latitude-longitude file (see data product). A ReadMe file additionally lists the metadata and recording parameters for the seismic profile. The data set is grouped into three categories: 1. Raw data comprise (a) demultiplexed seismic data in seg-y format, (b) a shot list of date, time and shot-number, and (c) navigation data with date, time, and coordinates. 2. Primary data consist of CDP-sorted data in seg-y format. 3. The data product is provided as (a) stacked or time-migrated version of the seismic profile in seg-y format and (b) a list with CDP and SHOT numbers as well as geographical coordinates. Please note that the low-frequency reflector that parallels the sea floor at depths of about 190 ms two-way travewltime (~150 m) is an artifact which has not been removed.
DOI: 10.1594/pangaea.963111
title: Basic measurements of radiation at station Reunion Island, University (2023-09)
DOI: 10.1594/pangaea.964356
title: Basic measurements of radiation at station Reunion Island, University (2023-11)
DOI: 10.1594/pangaea.963559
title: Basic measurements of radiation at station Reunion Island, University (2023-10)
DOI: 10.1594/pangaea.962349
title: Stable isotope (δ18О, δ2H and dexc) values for ice wedges exposed in yedoma deposits in the north of Western Siberia

description: Stable isotope (δ18О, δ2H and dexc) values for ice wedges exposed in yedoma deposits in the area of the Seyakha settlement, east coast of the Yamal Peninsula. Stable isotope (oxygen and hydrogen) analysis in ice-wedge ice was carried out in the Stable isotope laboratory of the Geography Faculty at Lomonosov Moscow State University using a Finnigan Delta-V Plus mass spectrometer. Analytical precision was ±0.4‰ for δ18O and ±1‰ for δ2H. Oxygen isotope measurements of some ice wedges sampled before 2016 were carried out using a G-50 device at the isotope hydrology laboratory at the Institute of Water Problems of Russian Academy of Science (Dr. A. Esikov), at the isotope geology laboratory at the Institute of Geology, Tallinn, Estonia (Prof. R. Vaikmäe), at the Isotope Laboratory of Helsinki University (E. Sonninen and Prof. H. Jungner), and in the Hannover Isotope Laboratory (Prof. M. Geyh). All values are presented in δ-notation in per mille (‰) relative to the Vienna Standard Mean Ocean Water (VSMOW).
DOI: 10.1594/pangaea.964638
title: Radiosonde measurements from station Tateno (2023-10)
DOI: 10.1594/pangaea.964639
title: Meteorological synoptical observations from station Tateno (2023-10)
DOI: 10.1594/pangaea.964880
title: Ozone measurements from station Tamanrasset (2023-12)
DOI: 10.1594/pangaea.964987
title: Meteorological synoptical observations from station Izaña (2023-12)
DOI: 10.1594/pangaea.964988
title: Ozone measurements from station Izaña (2023-11)
DOI: 10.1594/pangaea.964859
title: X-ray diffraction profiles, Raman spectra and carbon and oxygen isotope compositions of fibrous cement from Black Sea seeps

description: The mineral phase of primary marine fibrous carbonate is supposed to be mainly controlled by the Mg/Ca of seawater during precipitation. However, fibrous Mg calcite was identified from seep carbonates in the Black Sea, although the high Mg/Ca of the Black Sea seawater should promote the precipitation of aragonite. The unexpected formation of fibrous Mg calcite indicates additional factors other than Mg/Ca. In order to clarify the factors, we studied seep carbonates from the Black Sea. Points of interest were selected by observation under a microscope. Main compositions of the points were measured by an energy-dispersive X-ray spectroscope. Mineral structure and spectroscopy characteristics were analysed by X-ray diffraction and Raman, respectively, and converted to MgCO₃ contents according to empirical formulas. Carbon and oxygen isotope compositions of fibrous carbonate minerals were measured by an isotope ratio mass spectrometer. The results suggest that the fibrous Mg calcite from the Black Sea were formed by sulphide-driven anaerobic oxidation of methane under reducing conditions, which contributes to the understanding of the formation of Neoproterozoic fibrous dolomite.
description: Funding information:K. C. Wong Education Foundation: http://dx.doi.org/10.13039/501100012692DAAD: http://dx.doi.org/10.13039/501100001655Guangzhou Elite Project (No. JY201223)Open Access Publication Fund of Universität Hamburg: http://dx.doi.org/10.13039/501100005711
DOI: 10.1594/pangaea.964989
title: Ultra-violet measurements from station Izaña (2023-12)
DOI: 10.1594/pangaea.964991
title: Radiosonde measurements from station Izaña (2023-12)

description: Start location: Guimar; Distance from radiation site: 5 km
DOI: 10.1594/pangaea.964992
title: Radiosonde measurements from station Izaña (2023-11)

description: Start location: Guimar; Distance from radiation site: 5 km
DOI: 10.1594/pangaea.964993
title: Meteorological synoptical observations from station Izaña (2023-11)
DOI: 10.1594/pangaea.964994
title: Ozone measurements from station Izaña (2023-12)
DOI: 10.1594/pangaea.964995
title: Ultra-violet measurements from station Izaña (2023-11)
DOI: 10.6084/m9.figshare.22255873.v5
title: Labeled 17 Hardwood Species and 55 Genotypes of Populus Stomatal Images Datasets

description: Research has indicated the potential of using machine learning algorithms to detect and measure stomata automatically. However, the current limitation for further improving and fine-tuning machine learning-based stomatal study methods is due to the small, inconsistent, and monotypic nature of stomatal datasets, which are also not easily accessible. To address this issue, our collection comprises about 11,000 unique images of hardwood leaf stomata gathered from projects conducted between 2015 and 2020-2022. The dataset includes over 7,000 images of 17 frequently encountered hardwood species, including oak, maple, ash, elm, and hickory, as well as over 3,000 images of 55 genotypes from seven Populus taxa (as detailed in Table 1). Each image has been labeled as either stomata (stomatal aperture only) or whole_stomata (stomatal aperture and guard cells) and has a corresponding YOLO label file that can be transformed to other annotation formats. These images and labels are publicly available, making it easier to train machine-learning models and examine leaf stomatal traits. By utilizing our dataset, users can (1) use state-of-the-art machine learning models to identify, count, and quantify leaf stomata; (2) investigate the diverse range of stomatal characteristics across different types of hardwood trees; and (3) create new indices for measuring stomata.
DOI: 10.6084/m9.figshare.22255873
title: Labeled 17 Hardwood Species and 55 Genotypes of Populus Stomatal Images Datasets

description: Research has indicated the potential of using machine learning algorithms to detect and measure stomata automatically. However, the current limitation for further improving and fine-tuning machine learning-based stomatal study methods is due to the small, inconsistent, and monotypic nature of stomatal datasets, which are also not easily accessible. To address this issue, our collection comprises about 11,000 unique images of hardwood leaf stomata gathered from projects conducted between 2015 and 2020-2022. The dataset includes over 7,000 images of 17 frequently encountered hardwood species, including oak, maple, ash, elm, and hickory, as well as over 3,000 images of 55 genotypes from seven Populus taxa (as detailed in Table 1). Each image has been labeled as either <i>inner_guard_cell_walls</i> or <i>whole_stomata</i> (stomatal aperture and guard cells) and has a corresponding YOLO label file that can be transformed to other annotation formats. These images and labels are publicly available, making it easier to train machine-learning models and examine leaf stomatal traits. By utilizing our dataset, users can (1) use state-of-the-art machine learning models to identify, count, and quantify leaf stomata; (2) investigate the diverse range of stomatal characteristics across different types of hardwood trees; and (3) create new indices for measuring stomata.
DOI: 10.26179/brg3-0f65
title: 2012-13 Season Voyage Track and Underway Data
DOI: 10.6073/pasta/82f6becbd659b3ef4656fc0cd1d7d73c
title: Mangrove leaf physiological response to local climate at Key Largo, Watson River Chickee, Taylor Slough, and Little Rabbit Key, South Florida (FCE) from July 2001 to August 2001

description: Determine the red mangrove leaf physiological response to the local climate to understand the local controls on plant physiology. Data were collected in the Key Largo Ranger Station, Watson River Chickee and Taylor Slough research Sites, South Florida.
DOI: 10.34740/kaggle/dsv/7667140
title: WallStreetBets 2022
DOI: 10.5281/zenodo.10685643
title: Tabelas de países produtores de filmes (2011-2021)

description: Dados das Tabelas 1 e 2 foram organizados a partir das publicações FOCUS anuais do European Audiovisual Observatory (OBS).

Parte da pesquisa de Doutorado: Formações genéricas no filme de crime latino-americano contemporâneo

Com financiamento da Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) (processo nº. 21/05701-3).
DOI: 10.5281/zenodo.10685644
title: Tabelas de países produtores de filmes (2011-2021)

description: Dados das Tabelas 1 e 2 foram organizados a partir das publicações FOCUS anuais do European Audiovisual Observatory (OBS).

Parte da pesquisa de Doutorado: Formações genéricas no filme de crime latino-americano contemporâneo

Com financiamento da Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) (processo nº. 21/05701-3).
DOI: 10.6073/pasta/04d96a23b5e5dc8dba846f3a40a0f427
title: Inventory of soil prokaryotic microbiome (via 16S based on rRNA gene amplicons) in freshwater and brackish water marshes following saltwater intrusion along Shark River Slough boundary, Everglades National Park (FCE LTER), Florida, USA, September 2018

description: Global sea-level rise is transforming coastal ecosystems, especially freshwater wetlands, in part due to increased episodic or chronic saltwater exposure, leading to shifts in microbial communities and related ecological services. Soil prokaryotes play a fundamental role in regulating important biogeochemical processes in coastal wetland ecosystem. Yet, it is still difficult to predict how soil prokaryotic communities respond to the saltwater exposure because of poorly understood prokaryotic sensitivity within complex wetland soil microbial communities, as well as the high heterogeneity of wetland soils and saltwater exposure. To address this, a four-year experimental simulation of saltwater intrusion in a pristine freshwater site and a previously saltwater-impacted site was conducted. The saltwater addition started in October 2014 on a monthly basis and continued through October 2018. The dataset contains amplicon sequencing date of 16S rRNA gene obtained from saltwater-exposed soils and unmanipulated native soils in both sites (collected in September 2018). The 2018 data are published in Zhao et al. 2023. A detailed list of sequence data and their accession numbers in GenBank is provided, and data collection is complete.
 This data package is an inventory of sequence read archive (SRA) entries available through GenBank BioProject PRJNA804545 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA804545).
 This data package is associated with the following publication:
 Zhao, J., Chakrabarti, S., Chambers, R., Weisenhorn, P., Travieso, R., Stumpf, S., Standen, E., Briceno, H., Troxler, T., Gaiser, E., Kominoski, J., Dhillon, B., & Martens-Habbena, W. (2023). Year-around survey and manipulation experiments reveal differential sensitivities of soil prokaryotic and fungal communities to saltwater intrusion in Florida Everglades wetlands. Science of The Total Environment, 858, 159865. https://doi.org/10.1016/j.scitotenv.2022.159865
 Instead of citing this package, which is an inventory, please cite the original GenBank data or journal article, as appropriate. Citation guidance for the journal article is available on the respective publisher's website.
DOI: 10.5281/zenodo.10685648
title: Dataset for "Development of an automated reliable method to compute transport properties from DPD equilibrium simulations: Application to simple fluids."

description: Dataset for figures and tables of the paper "Development of an automated reliable method to compute transport properties from DPD equilibrium simulations: Application to simple fluids."
DOI: 10.5281/zenodo.8290016
title: Dataset for "Development of an automated reliable method to compute transport properties from DPD equilibrium simulations: Application to simple fluids."

description: Dataset for figures and tables of the paper "Development of an automated reliable method to compute transport properties from DPD equilibrium simulations: Application to simple fluids."
DOI: 10.5281/zenodo.8290017
title: Dataset for "Development of an automated reliable method to compute transport properties from DPD equilibrium simulations: Application to simple fluids."

description: Dataset for figures and tables of the paper "Development of an automated reliable method to compute transport properties from DPD equilibrium simulations: Application to simple fluids."