cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.13145/bacdive15579.20170829.2
title: Streptomyces scabiei (ex Thaxter 1891) Lambert and Loria 1989

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive11494.20170829.2
title: Paenibacillus validus (Nakamura 1984) Ash et al. 1994 emend. Heyndrickx et al. 1995

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive11495.20170829.2
title: Paenibacillus glucanolyticus (Alexander and Priest 1989) Shida et al. 1997

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive11496.20170829.2
title: Paenibacillus glucanolyticus (Alexander and Priest 1989) Shida et al. 1997

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive11497.20170829.2
title: Paenibacillus glucanolyticus (Alexander and Priest 1989) Shida et al. 1997

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive11498.20170829.2
title: Paenibacillus thiaminolyticus (Nakamura 1990) Shida et al. 1997

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive11499.20170829.2
title: Paenibacillus thiaminolyticus (Nakamura 1990) Shida et al. 1997

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive11500.20170829.2
title: Paenibacillus thiaminolyticus (Nakamura 1990) Shida et al. 1997

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive11501.20170829.2
title: Paenibacillus thiaminolyticus (Nakamura 1990) Shida et al. 1997

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive11502.20170829.2
title: Paenibacillus thiaminolyticus (Nakamura 1990) Shida et al. 1997

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive12348.20170829.2
title: Sorangium cellulosum (Brockmann 1989) Reichenbach 2007

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive12349.20170829.2
title: Sorangium cellulosum (Brockmann 1989) Reichenbach 2007

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive12350.20170829.2
title: Sorangium cellulosum (Brockmann 1989) Reichenbach 2007

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.21335/nmdc-290836407
title: Ocean temperatures in the northern Barents Sea for Nansen Legacy synthesis work

description: File “Fig_3a_Timeseries.xls” give mean late summer/early fall (August, September, October) subsurface (50-200 m) ocean temperature in the Northern Barents Sea (77-79.5oN, 25-50oE). Mean ocean temperature was calculated only when at least 80% of the ocean integration box was covered by observations (thus not in the heavy ice years of 2003 and 2014). The file “Fig_4_Horizontal_temperature_fields.xls” include mean temperature between 50 and 200 m depth in late summer (August, September, October) during the years 1985-1989 and 2012-2016. The two first data sheets give the coordinates of the grid of the data. The ocean temperatures in “Fig_3a_Timeseries.xls” and “Fig_4_Horizontal_temperature_fields.xls” are based on gridded CTD observations from annual joint Institute of Marine Research (IMR)–Polar branch of the FSBSI “VNIRO” (PINRO) surveys covering the entire Barents Sea in August-October. The file “Fig_5_Vardo-North_section_extended.xls” give mean temperature along 30-34°E in late summer (August, September, October) during the years 1985-1989 and 2012-2016 based on gridded observations from a repeated transect from Vardø (Norway) in the South to the Nansen Basin in the North. The data are sampled annually by Institute of Marine Research. The first sheet gives depth of observation while the second sheet give latitude.
DOI: 10.5281/zenodo.7427213
title: Multicomponent ocean-bottom seismic data decomposition using separate calibration filters

description: All relevant pictures in the article
DOI: 10.5281/zenodo.7427212
title: Multicomponent ocean-bottom seismic data decomposition using separate calibration filters

description: All relevant pictures in the article
DOI: 10.5517/cc132y6b
title: CCDC 1015721: Experimental Crystal Structure Determination

description: Related Article: J. Gabriel Garcia, Frank R. Fronczek|2014|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.21335/nmdc-416643420
title: Fish catch, stock abundance and harvest rates

description: File “Fig_11_catch_harvestrate_and_stock.xls” give annual catches, stock abundance and harvest rates for the main commercial Barents Sea fish stocks (including shrimp which is the main shellfish stock). The harvest rates are derived from catches and stock abundance. Catches are given from 1965 onwards for all stocks, while the time series of stock abundance and thus harvest rates start later than 1965 for several stocks.
DOI: 10.21335/nmdc-2002594157
title: Pelagic biomasses in the northern and southern Barents Sea for the period 1993-2013

description: Estimated pelagic biomasses (million tonnes wet weight) of pelagic fish stocks (capelin, herring, blue whiting, and polar cod) and 0-group fishes (cod, herring, haddock, polar cod, redfish, Greenland halibut, long rough dab, wolffish, blue whiting, and saithe) and macroplankton (mainly krill) in the northern and southern Barents Sea for the period 1993-2013. The background data were taken from [1]). Data for biomass calculation were collected during surveys: Trawl data for pelagic fish, 0-group fish, macroplankton were collected during a joint Norwegian-Russian 0-group fish trawl survey in August-September 1993-2003 using a small-meshed pelagic trawl (“Harstad”) with 20m*20m mouth opening [2]. Acoustic data for pelagic stocks (capelin, herring, blue whiting, and polar cod) were collected in September-October 1993-2003. Acoustic data are integrated continuously along the survey tracks, and mean values of acoustic back-scattering per nautical mile (nm) are recorded for mapping and calculations of fish biomass. The echo sounders are monitored continuously, and trawl hauls (“Harstad” trawl) are carried out whenever the recordings change their characteristics and/or the need for biological data makes it necessary [3]. Since 2004, both the 0-group trawl survey and the acoustic capelin survey have been part of a Barents Sea ecosystem survey (BESS, [4, 5]). The timing of the BESS allows access by research vessels to most of or the whole Barents Sea, sea-ice being at its seasonal minimum. In August-September, migratory species such as capelin have reached their maximum northern distribution at the end of the summer feeding period. At this time of year, the juvenile 0-group fish of commercially and ecologically important species are sufficiently large to be caught by trawl, while 0-group demersal species such as cod and haddock have not yet settled to the bottom. For further description of the sample processing, determination of biomass, and data series see [1].
DOI: 10.5281/zenodo.7419274
title: data for [Petitdemange, Marcotte, Gissinger (ArXiv 2022)]

description: <em>This zenodo repository provides the necessary material to reproduce the results reported in Petitdemange, Marcotte, Gissinger (arXiv 2022), (https://arxiv.org/abs/2206.13819).</em>
DOI: 10.5281/zenodo.7419273
title: data for [Petitdemange, Marcotte, Gissinger (ArXiv 2022)]

description: <em>This zenodo repository provides the necessary material to reproduce the results reported in Petitdemange, Marcotte, Gissinger (arXiv 2022), (https://arxiv.org/abs/2206.13819).</em>
DOI: 10.5281/zenodo.6997231
title: Refined Land cover for Beijing, Shanghai, Ningbo in China and Paris Region, Velika Gorica, Aarhus in Europe under different scenarios in 2030

description: Europe and China Refined Land Cover 2030 (ECRLC2030) was derived from historical landcover observations, natural geographical, location, and socio-economic factors and the Conversion of Land Use and its Effects at Small Regional Extent model (CLUE-S). With a spatial resolution of 60m, landcover under three different scenarios were simulated: the business-as-usual scenario (BAU), the market-liberal scenario (MLS), and the ecological protection scenario (EPS).
description: {"references": ["Wu, W., Luo, X., Knopp, J., Jones, L., & Banzhaf, E. (2022). A European-Chinese Exploration: Part 2\u2014Urban Ecosystem Service Patterns, Processes, and Contributions to Environmental Equity under Different Scenarios. Remote Sensing, 14(14), 3488.", "Banzhaf, E., Wu, W., Luo, X., & Knopp, J. (2021). Integrated Mapping of Spatial Urban Dynamics\u2014A European-Chinese Exploration. Part 1\u2014Methodology for Automatic Land Cover Classification Tailored towards Spatial Allocation of Ecosystem Services Features. Remote Sensing, 13(9), 1744."]}
DOI: 10.5281/zenodo.6997232
title: Refined Land cover for Beijing, Shanghai, Ningbo in China and Paris Region, Velika Gorica, Aarhus in Europe under different scenarios in 2030

description: Europe and China Refined Land Cover 2030 (ECRLC2030) was derived from historical landcover observations, natural geographical, location, and socio-economic factors and the Conversion of Land Use and its Effects at Small Regional Extent model (CLUE-S). With a spatial resolution of 60m, landcover under three different scenarios were simulated: the business-as-usual scenario (BAU), the market-liberal scenario (MLS), and the ecological protection scenario (EPS).
description: {"references": ["Wu, W., Luo, X., Knopp, J., Jones, L., & Banzhaf, E. (2022). A European-Chinese Exploration: Part 2\u2014Urban Ecosystem Service Patterns, Processes, and Contributions to Environmental Equity under Different Scenarios. Remote Sensing, 14(14), 3488.", "Banzhaf, E., Wu, W., Luo, X., & Knopp, J. (2021). Integrated Mapping of Spatial Urban Dynamics\u2014A European-Chinese Exploration. Part 1\u2014Methodology for Automatic Land Cover Classification Tailored towards Spatial Allocation of Ecosystem Services Features. Remote Sensing, 13(9), 1744."]}
DOI: 10.48366/r274032
title: X-ray observations from cosmic objects 1: planets

description: In this comparison a set of publications are presented from the field of X-ray Astronomy, in particular for solar system planets. This field has seen an increased interest since the launch of space observatories like Chandra X-ray Observatory and XMM - Newton. This is the first comparison of a set that will cover: planets, comets, the sun, the stars and warm-hot intergalactic medium. This set is also a direct evolution from published comparisons about the complementary topic of laboratory X-ray measurements in Highly Charged Ions (HCIs).
DOI: 10.6084/m9.figshare.21299880
title: NPPAD: An Open Time-series Dataset Covering Various Accidents for Nuclear Power Plants

description: The dataset, namely Nuclear Power Plant Accident Data (NPPAD), basically covers most of the common types of accidents that can occur in typical pressurized water reactor nuclear power plants.
DOI: 10.6084/m9.figshare.21299880.v1
title: NPPAD: An Open Time-series Dataset Covering Various Accidents for Nuclear Power Plants

description: The dataset, namely Nuclear Power Plant Accident Data (NPPAD), basically covers most of the common types of accidents that can occur in typical pressurized water reactor nuclear power plants.
DOI: 10.5281/zenodo.7427375
title: The 30 m long-term LAke Water Secchi Depth (SD) dataset (LAWSD30) of China (1985–2020)

description: Monitoring the water clarity of lakes is essential for the sustainable development of human society. However, existing water clarity assessments in China have mostly focused on lakes with areas &gt; 1 km<sup>2</sup>, and the monitoring periods were mainly in the 21st century. In order to improve the understanding of spatiotemporal variations in lake clarity across China, based on the Google Earth Engine (GEE) cloud platform, a 30 m long-term LAke Water Secchi Depth (SD) dataset (LAWSD30) of China (1985–2020) was developed using Landsat series imagery and a robust water-color-parameter-based SD model. <strong><em>Noted, the LAWSD30 has been updated to 2021 using Landsat data from 2020 to 2022.</em></strong> The details are described in "<em>30 m long-term LAke Water Secchi depth (SD) dataset (LAWSD30) of China (1985–2021)_Readme_V1.1.docx</em>".
description: {"references": ["Chen, X., Liu, L., Zhang, X., Li, J., Wang, S., Gao, Y., and Mi, J. (2022). Long-term water clarity patterns of lakes across China using Landsat series imagery from 1985 to 2020. Hydrol. Earth Syst. Sci., 26, 3517\u20133536.", "Chen, X., Liu, L., Zhang, X., Li, J., & Song, K. (2021). An assessment of water color for inland water in China using a Landsat 8-derived Forel-Ule index and the Google Earth Engine platform. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, PP(99), 1-1"]}
DOI: 10.5281/zenodo.5734070
title: The 30 m long-term LAke Water Secchi Depth (SD) dataset (LAWSD30) of China (1985–2020)

description: Monitoring the water clarity of lakes is essential for the sustainable development of human society. However, existing water clarity assessments in China have mostly focused on lakes with areas &gt; 1 km<sup>2</sup>, and the monitoring periods were mainly in the 21st century. In order to improve the understanding of spatiotemporal variations in lake clarity across China, based on the Google Earth Engine (GEE) cloud platform, a 30 m long-term LAke Water Secchi Depth (SD) dataset (LAWSD30) of China (1985–2020) was developed using Landsat series imagery and a robust water-color-parameter-based SD model. <strong><em>Noted, the LAWSD30 has been updated to 2021 using Landsat data from 2020 to 2022.</em></strong> The details are described in "<em>30 m long-term LAke Water Secchi depth (SD) dataset (LAWSD30) of China (1985–2021)_Readme_V1.1.docx</em>".
description: {"references": ["Chen, X., Liu, L., Zhang, X., Li, J., Wang, S., Gao, Y., and Mi, J. (2022). Long-term water clarity patterns of lakes across China using Landsat series imagery from 1985 to 2020. Hydrol. Earth Syst. Sci., 26, 3517\u20133536.", "Chen, X., Liu, L., Zhang, X., Li, J., & Song, K. (2021). An assessment of water color for inland water in China using a Landsat 8-derived Forel-Ule index and the Google Earth Engine platform. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, PP(99), 1-1"]}
DOI: 10.5281/zenodo.7424166
title: Toolbox Bouquet - PracticalMEEG 2022 - NeuroPycon & ephypype : Dataset

description: The dataset analyzed in the hands-on session contains 1 participant of the FACE dataset from Wakeman and Henson (2015). The data consist of simultaneous MEG/EEG recordings from 19 healthy participants performing a visual recognition task. Subjects were presented images of famous, unfamiliar and scrambled faces. Each subject participated in 6 runs, each 7.5 min in duration. During the hands-on, we will focus only on MEG data and use NeuroPycon pipelines to preprocess the MEG raw data and to perform source reconstruction of time-locked event-related fields. For installation and more info look at the workshop page. The two files (ds000117.tar.gz, ds000117_light.tar.gz) contain the MEG and MRI data for the same subject but in light you will find a shorter version of the raw MEG data.
DOI: 10.5281/zenodo.7424167
title: Toolbox Bouquet - PracticalMEEG 2022 - NeuroPycon & ephypype : Dataset

description: The dataset analyzed in the hands-on session contains 1 participant of the FACE dataset from Wakeman and Henson (2015). The data consist of simultaneous MEG/EEG recordings from 19 healthy participants performing a visual recognition task. Subjects were presented images of famous, unfamiliar and scrambled faces. Each subject participated in 6 runs, each 7.5 min in duration. During the hands-on, we will focus only on MEG data and use NeuroPycon pipelines to preprocess the MEG raw data and to perform source reconstruction of time-locked event-related fields. For installation and more info look at the workshop page. The two files (ds000117.tar.gz, ds000117_light.tar.gz) contain the MEG and MRI data for the same subject but in light you will find a shorter version of the raw MEG data.
DOI: 10.5281/zenodo.5734071
title: The 30 m long-term LAke Water Secchi Depth (SD) dataset (LAWSD30) of China (1985–2020)

description: Monitoring the water clarity of lakes is essential for the sustainable development of human society. However, existing water clarity assessments in China have mostly focused on lakes with areas &gt; 1 km<sup>2</sup>, and the monitoring periods were mainly in the 21st century. In order to improve the understanding of spatiotemporal variations in lake clarity across China, based on the Google Earth Engine (GEE) cloud platform, a 30 m long-term LAke Water Secchi Depth (SD) dataset (LAWSD30) of China (1985–2020) was developed using Landsat series imagery and a robust water-color-parameter-based SD model. <strong><em>Noted, the LAWSD30 has been updated to 2021 using Landsat data from 2020 to 2022.</em></strong> The details are described in "<em>30 m long-term LAke Water Secchi depth (SD) dataset (LAWSD30) of China (1985–2021)_Readme_V1.1.docx</em>". <strong>Please see Version V1.1</strong>.
description: {"references": ["Chen, X., Liu, L., Zhang, X., Li, J., Wang, S., Gao, Y., and Mi, J. (2022). Long-term water clarity patterns of lakes across China using Landsat series imagery from 1985 to 2020. Hydrol. Earth Syst. Sci., 26, 3517\u20133536.", "Chen, X., Liu, L., Zhang, X., Li, J., & Song, K. (2021). An assessment of water color for inland water in China using a Landsat 8-derived Forel-Ule index and the Google Earth Engine platform. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, PP(99), 1-1"]}
DOI: 10.5281/zenodo.6638774
title: DATA: Comparison between optical tissue clearing methods for detecting administered mesenchymal stromal cells in mouse lungs

description: This data set includes all the raw data collected for the following article: "Comparison between optical tissue clearing methods for detecting administered mesenchymal stromal cells in mouse lungs".
description: This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Actions, grant agreement No 813839, Innovative Training Network RenalToolBox.
DOI: 10.5281/zenodo.6638775
title: DATA: Comparison between optical tissue clearing methods for detecting administered mesenchymal stromal cells in mouse lungs

description: This data set includes all the raw data collected for the following article: "Comparison between optical tissue clearing methods for detecting administered mesenchymal stromal cells in mouse lungs".
description: This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Actions, grant agreement No 813839, Innovative Training Network RenalToolBox.
DOI: 10.5281/zenodo.7410708
title: Data for Playlist Captioning With Musical and Linguistic Knowledge

description: We release a new playlists dataset that can be used to reproduce the experiments from the article <em>Data-Efficient Playlist Captioning With Musical and Linguistic Knowledge</em> accepted at the EMNLP 2022 conference. More information about this data and how it should be used in experiments can be found in our paper and GitHub repository. Please cite our paper if you use the code or data.
DOI: 10.5281/zenodo.7410709
title: Data for Playlist Captioning With Musical and Linguistic Knowledge

description: We release a new playlists dataset that can be used to reproduce the experiments from the article <em>Data-Efficient Playlist Captioning With Musical and Linguistic Knowledge</em> accepted at the EMNLP 2022 conference. More information about this data and how it should be used in experiments can be found in our paper and GitHub repository. Please cite our paper if you use the code or data.
DOI: 10.5281/zenodo.7418837
title: Data for Playlist Captioning With Musical and Linguistic Knowledge

description: We release a new playlists dataset that can be used to reproduce the experiments from the article <em>Data-Efficient Playlist Captioning With Musical and Linguistic Knowledge</em> accepted at the EMNLP 2022 conference. More information about this data and how it should be used in experiments can be found in our paper and GitHub repository. Please cite our paper if you use the code or data.
DOI: 10.5281/zenodo.7419490
title: Data for Playlist Captioning With Musical and Linguistic Knowledge

description: We release a new playlists dataset that can be used to reproduce the experiments from the article <em>Data-Efficient Playlist Captioning With Musical and Linguistic Knowledge</em> accepted at the EMNLP 2022 conference. More information about this data and how it should be used in experiments can be found in our paper and GitHub repository. Please cite our paper if you use the code or data.
DOI: 10.5281/zenodo.7427231
title: Data for Playlist Captioning With Musical and Linguistic Knowledge

description: We release a new playlists dataset that can be used to reproduce the experiments from the article <em>Data-Efficient Playlist Captioning With Musical and Linguistic Knowledge</em> accepted at the EMNLP 2022 conference. More information about this data and how it should be used in experiments can be found in our paper and GitHub repository. Please cite our paper if you use the code or data.
DOI: 10.5281/zenodo.6701351
title: PySME - Paper Supplementary Data

description: Synthetic spectra created for the paper "PySME - Spectroscopy Made Easier". For more information on the creation of these spectra, see the paper. Based on observations collected at the European Southern Observatory under ESO programs 60.A-9036(A), 192.C-0224(A), 083.C-0794(A), 072.C-0488(E), 288.C-5010(A), 0104.C-0849(A), and 098.C-0739(A).
DOI: 10.5281/zenodo.7427483
title: PySME - Paper Supplementary Data

description: Synthetic spectra created for the paper "PySME - Spectroscopy Made Easier". For more information on the creation of these spectra, see the paper. Based on observations collected at the European Southern Observatory under ESO programs 60.A-9036(A), 192.C-0224(A), 083.C-0794(A), 072.C-0488(E), 288.C-5010(A), 0104.C-0849(A), and 098.C-0739(A).
DOI: 10.5281/zenodo.6701350
title: PySME - Paper Supplementary Data

description: Synthetic spectra created for the paper "PySME - Spectroscopy Made Easier". For more information on the creation of these spectra, see the paper. Based on observations collected at the European Southern Observatory under ESO programs 60.A-9036(A), 192.C-0224(A), 083.C-0794(A), 072.C-0488(E), 288.C-5010(A), 0104.C-0849(A), and 098.C-0739(A).