cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5066/p9hqhdmh
title: Thermal camera data for the summit of K?lauea Volcano, 2019?2022

description: Following the 2018 collapses of the caldera floor at the summit of K?lauea Volcano (Anderson and others, 2019; Neal and others, 2019), the enlarged and deepened depression hosted a variety of volcanic activity between 2019 and 2022. These events included an unprecedented water lake and two prolonged episodes of lava lake activity. This data release includes images from a stationary thermal camera poised on the western caldera rim, with the camera providing a continuous record of the summit changes over this period. The thermal images provide an excellent observational record of the activity owing to the ability to see through thick volcanic fume, and the clarity with which they highlight active portions of the lava lake (Patrick and others, 2014). These thermal camera images cover three phases of activity at the summit of K?lauea. First, a water lake was present deep in Halema?uma?u crater from July 2019 to December 2020 (Nadeau and others, 2020). Second, a rising lava lake filled the bottom of Halema?uma?u from December 2020 to May 2021. Third, a second lava lake filled more of Halema?uma?u from September 2021 into early 2022 (through the end of this data release period in January 2022). References Anderson, K.R., Johanson, I.A., Patrick, M.R., Gu, M., Segall, P., Poland, M.P., Montgomery-Brown, E.K., and Miklius, A., 2019, Magma reservoir failure and the onset of caldera collapse at K?lauea Volcano in 2018: Science, doi:10.1126/science.aaz1822 Nadeau, P.A., Diefenbach, A.K., Hurwitz, S., and Swanson, D.A., 2020, From lava to water: A new era at Kilauea: Eos, 101, doi:10.1029/2020EO149557 Neal, C.A., Brantley, S.R., Antolik, L., Babb, J., Burgess, M., Calles, K., Cappos, M., Chang, J.C., Conway, S., Desmither, L., Dotray, P., Elias, T., Fukunaga, P., Fuke, S., Johanson, I.A., Kamibayashi, K., Kauahikaua, J., Lee, R.L., Pekalib, S., Miklius, A., Million, W., Moniz, C.J., Nadeau, P.A., Okubo, P., Parcheta, C., Patrick, M.R., Shiro, B,, Swanson, D.A., Tollett, W., Trusdell, F., Younger, E.F., Zoeller, M.H., Montgomery-Brown, E.K., Anderson, K.R., Poland, M.P., Ball, J., Bard, J., Coombs, M., Dietterich, H.R., Kern, C., Thelen, W.A., Cervelli, P.F., Orr, T., Houghton, B.F., Gansecki, C., Hazlett, R., Lundgren, P., Diefenbach, A.K., Lerner, A.H., Waite, G., Kelly, P., Clor, L., Werner, C., Mulliken, K., and Fisher, G. 2018. The 2018 rift eruption and summit collapse of Kilauea Volcano. Science, doi:10.1126/science.aav7046. Patrick, M., Orr, T., Antolik, L., Lee, L., and Kamibayashi, K., 2014, Continuous monitoring of Hawaiian volcanoes with thermal cameras: Journal of Applied Volcanology, v. 3, no. 1. http://www.appliedvolc.com/content/3/1/ Patrick, M.R., Swanson, D.A., Zoeller, M.H., Mulliken, K.M., Parcheta, C.E., Lynn, K.J., Downs, D.T., and Flinders, A.F., 2021, Water-level data for the crater lake at the summit of K?lauea Volcano, Island of Hawai?i, 2019?2020: U.S. Geological Survey data release, https://doi.org/10.5066/P9262JDH.
DOI: 10.5281/zenodo.6390740
title: MIRA benchmarking Frankencell datasets

description: MIRA benchmarking Frankencell datasets, scaffolds, and configuration file for regeneration.
DOI: 10.5281/zenodo.6386770
title: MIRA benchmarking Frankencell datasets

description: MIRA benchmarking Frankencell datasets, scaffolds, and configuration file for regeneration.
DOI: 10.6073/pasta/676ca962d375e90b1d06089c0cac88ba
title: Surface and SubSurface Soil Organic Matter Processing following Hurricane Harvey, Texas, USA

description: Coastal wetland plant identity and cover is changing, as many subtropical salt marshes dominated by low-stature herbaceous species transition to woody mangroves. How changes in dominant plant species affect carbon processing in coastal wetlands during storms is uncertain. We experimentally manipulated patch-scale (3 × 3 m) cover of black mangroves (Avicennia germinans) and saltmarsh plants (e.g., Spartina alterniflora, Batis maritima) in fringe and interior locations of ten plots (24 × 42 m) to create a gradient in mangrove cover in coastal Texas, USA. Hurricane Harvey made direct landfall over our site on 25 August 2017. To test how mangrove cover affected carbon retention after the storm, we measured litter breakdown rates (k) of A. germinans and S. alterniflora in surface soils and fast- and slow-decomposing standard litter substrates (green and red tea, respectively) in subsurface soils (15 cm depth). Soil temperatures were lower in mangrove than marsh patches, and prior microclimate measurements showed non-linear increases in air and soil temperatures with increasing mangrove cover (highest temperatures at intermediate % cover). Litter breakdown rates (k) were 2 higher in surface than in subsurface soils. Avicennia germinans litter k increased linearly in surface soils with plot-level mangrove cover, whereas slow-decomposing red tea had similar k in subsurface soils of all plots. Litter k of S. alterniflora in surface soils and fast-decomposing green tea in subsurface soils increased non-linearly with mangrove cover (highest k at intermediate % cover), explained largely by temperature. Microbial respiration rates (R) were highest in interior marsh patches for S. alterniflora litter and increased with plot-level mangrove cover, whereas R associated with A. germinans litter was similar among fringe and interior patches and highest at higher mangrove cover. Despite widespread declines in soil nutrient concentrations throughout marsh and mangrove patches in all plots, most surface and subsurface rates of carbon processing increased with mangrove cover in coastal wetlands following a hurricane.
DOI: 10.14469/hpc/9196
title: C6H5CF3-DMF-B3LYP/aug-cc-pvdz

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/9197
title: C6H5CF3-DMF-MP2/aug-cc-pvdz/NMR

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/9198
title: C6H5CF3-DMF-MP2/aug-cc-pvdz/OPT

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/9346
title: BF3*THF-B3LYP+GD3BJ/aug-cc-pvdz

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/9347
title: Fluoroform-Acetone-wb97xd/def2svpp

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/9348
title: Fluoroform-Acetone-wb97xd/aug-pcSseq1

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/9349
title: Fluoroform-Acetone-B3LYP+GD3BJ/def2svpp

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/9355
title: Fluoroform-DCM-MP2/aug-cc-pvdz/NMR

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/9356
title: Fluoroform-DMSO-wb97xd/def2svpp

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/9357
title: Fluoroform-Benzene-B3LYP+GD3BJ/def2svpp

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/9358
title: Fluoroform-Benzene-B3LYP/def2svpp

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/9359
title: Fluoroform-Benzene-B3LYP+GD3BJ/aug-cc-pvdz

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/9360
title: Fluoroform-Benzene-B3LYP/aug-cc-pvdz

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/9361
title: Fluoroform-Benzene-MP2/aug-cc-pvdz/NMR

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/9362
title: Fluoroform-Benzene-MP2/aug-cc-pvdz/OPT

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/9364
title: Fluoroform-Benzene-wb97xd/def2svpp

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/9372
title: Fluoroform-Acetonitrile-B3LYP+GD3BJ/aug-cc-pvdz

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/9389
title: p-C6H4F2-hexane-B3LYP+GD3BJ/def2svpp

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/9390
title: p-C6H4F2-hexane-B3LYP/def2svpp

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/9593
title: p-C6H4F2-Acetone-wb97xd/aug-cc-pvdz

description: Gaussian 16 (C01) calculation
DOI: 10.6084/m9.figshare.19435049.v1
title: GHM_code.zip

description: The code of GHM interpolation.
DOI: 10.6084/m9.figshare.19435049
title: GHM_code.zip

description: The code of GHM interpolation.
DOI: 10.15482/usda.adc/1524789
title: Honey bee (Apis mellifera) and Varroa mite (Varroa destructor) toxicity data
DOI: 10.5281/zenodo.5874765
title: L3Pilot Open Data

description: The L3Pilot Open Data contains processed data collected during the Piloting of pre-series automated prototype vehicles on public European roads. The dataset contains driving data in the form of performance indicators derived for all instances of certain driving scenarios such as Car Following or lane changes. Furthermore, it contains data from the questionnaires handed to both professional and ordinary driver piloting the vehicles. All data is provided as comma separated tables. The supplementing document provides all necessary information for working with the dataset and mentions all documents, where additional information can be found. Already executed analysis based on the collected data, of which this represents a subset, can be found in Deliverable D7.3 - Pilot Evaluation Results, available for Download at l3pilot.eu/downloads<br> <br> Further publicly available dataset, such as trajectory data recorded with drones and an additional user survey, are linked on: l3pilot.eu/data
description: When working with the data, it is important to consider, that the data was gathered using pre-series vehicles in an experimental setup on public roads. Although the vehicles piloted were mature enough for operation on public roads, they may deviate from behaviour of automated series vehicles, which could for instance depend on legislative changes on speeds and distance requirements. This may also affect the user-related analysis.
description: {"references": ["Weber et al. (2021), L3Pilot Deliverable D7.3 - Pilot Evaluation Results", "Andreone et al. (2021), L3Pilot Deliverable D6.5 - Pilot Reporting Outcomes", "Metz et al. (2019), L3PIlot Deliverable D3.3 - Evaluation Methods", "Innamaa et al. (2020), L3Pilot Deliverable D3.4 - Evaluation Plan"]}
DOI: 10.5281/zenodo.5874764
title: L3Pilot Open Data

description: The L3Pilot Open Data contains processed data collected during the Piloting of pre-series automated prototype vehicles on public European roads. The dataset contains driving data in the form of performance indicators derived for all instances of certain driving scenarios such as Car Following or lane changes. Furthermore, it contains data from the questionnaires handed to both professional and ordinary driver piloting the vehicles. All data is provided as comma separated tables. The supplementing document provides all necessary information for working with the dataset and mentions all documents, where additional information can be found. Already executed analysis based on the collected data, of which this represents a subset, can be found in Deliverable D7.3 - Pilot Evaluation Results, available for Download at l3pilot.eu/downloads<br> <br> Further publicly available dataset, such as trajectory data recorded with drones and an additional user survey, are linked on: l3pilot.eu/data
description: When working with the data, it is important to consider, that the data was gathered using pre-series vehicles in an experimental setup on public roads. Although the vehicles piloted were mature enough for operation on public roads, they may deviate from behaviour of automated series vehicles, which could for instance depend on legislative changes on speeds and distance requirements. This may also affect the user-related analysis.
description: {"references": ["Weber et al. (2021), L3Pilot Deliverable D7.3 - Pilot Evaluation Results", "Andreone et al. (2021), L3Pilot Deliverable D6.5 - Pilot Reporting Outcomes", "Metz et al. (2019), L3PIlot Deliverable D3.3 - Evaluation Methods", "Innamaa et al. (2020), L3Pilot Deliverable D3.4 - Evaluation Plan"]}
DOI: 10.5281/zenodo.6390650
title: Self-generated gradients steer collective migration on viscoelastic collagen networks

description: Minimal Raw Data Set for: Self-generated gradients steer collective migration on viscoelastic collagen networks Clark et al, 2022
DOI: 10.5281/zenodo.6390649
title: Self-generated gradients steer collective migration on viscoelastic collagen networks

description: Minimal Raw Data Set for: Self-generated gradients steer collective migration on viscoelastic collagen networks Clark et al, 2022
DOI: 10.5281/zenodo.6373313
title: A Parameterized Model for Differential Galaxy Counts at Any Wavelength

description: Collected literature values of Schechter functions, resulting parameter values of fits to those data as a function of wavelength, and Python scripts to compute simulated differential galaxy counts based on those parameterisations. Future revisions of galaxy_counts.py can be found at https://github.com/Onoddil/macauff/blob/main/macauff/ galaxy counts.py, and future revisions of literature and parameterisations can be found at https://onoddil.github.io/galaxy evolution/galaxy counts.html.
DOI: 10.5281/zenodo.6373312
title: A Parameterized Model for Differential Galaxy Counts at Any Wavelength

description: Collected literature values of Schechter functions, resulting parameter values of fits to those data as a function of wavelength, and Python scripts to compute simulated differential galaxy counts based on those parameterisations. Future revisions of galaxy_counts.py can be found at https://github.com/Onoddil/macauff/blob/main/macauff/ galaxy counts.py, and future revisions of literature and parameterisations can be found at https://onoddil.github.io/galaxy evolution/galaxy counts.html.
DOI: 10.25387/g3.16973293.v1
title: Supplemental Material for Adams et al., 2022

description: This dataset contains two comma delimited files; one containing trial data (Phenotypes.csv) and the other crossing information (Pedigrees.csv). The former contains trial data from two Dutch field trials for 806 potato hybrid crosses. These were conducted in 2019 using an augmented randomised complete block design with two blocks for each trial. Field column (col), row (row), and block information are recorded for each data entry. Total tuber yield (TY; Mg/Ha), total tuber number (TN), and average tuber volume (TV; cm^3) are all recorded here. <br><br>The crossing schema related to the creation of diploid hybrid potato from several nurseries. The first column is the identifier for each hybrid with dam and sire being the identity of the maternal and paternal pure lines, respectively.
DOI: 10.25387/g3.16973293
title: Supplemental Material for Adams et al., 2022

description: This dataset contains two comma delimited files; one containing trial data (Phenotypes.csv) and the other crossing information (Pedigrees.csv). The former contains trial data from two Dutch field trials for 806 potato hybrid crosses. These were conducted in 2019 using an augmented randomised complete block design with two blocks for each trial. Field column (col), row (row), and block information are recorded for each data entry. Total tuber yield (TY; Mg/Ha), total tuber number (TN), and average tuber volume (TV; cm^3) are all recorded here. <br><br>The crossing schema related to the creation of diploid hybrid potato from several nurseries. The first column is the identifier for each hybrid with dam and sire being the identity of the maternal and paternal pure lines, respectively.
DOI: 10.6084/m9.figshare.19424015.v8
title: Neocortical Localization and Thalamocortical Modulation of Neuronal Hyperexcitability contribute to Fragile X Syndrome: Source Data for Figures and Statistics

description: Source data for manuscript as requested by publisher.
DOI: 10.5281/zenodo.6390870
title: Imaging topological defects in a non-collinear antiferromagnet

description: Data related to the publication, arXiv 2202.02243.
description: Other funding: ANR project TATOO (ANR-21-CE09-0033-01), DARPA TEE program, LABEX NanoSaclay, ref. ANR-10-LABX-0035, SPiCY, Sesame Ile de France IMAGeSPIN project (No. EX039175)
DOI: 10.5281/zenodo.6383195
title: Big in Japan, Zimbabwe or Brazil – global reach and national preferences for open access books

description: When we consider the main differences in publication practices between the Humanities and Social Sciences (HSS) and Science Technology and Mathematics (STM) there are two aspects that almost always get mentioned: the prominence of books and the role of local languages. Not surprisingly, the work of HSS researchers written in in other languages than English is more often linked to more regional concerns or a more regional community. Simply put, an author using Dutch will have a different audience in mind than an author writing in English. The OAPEN Library contains thousands of open access monographs in dozens of languages and sees global usage. While books in English are in the majority – over 60% of the collection – this still leaves a large collection of publications in other languages. If a global audience can freely choose from this collection, will there be a preference for global subjects or will more regional concerns take preference? This explorative research will look at the most popular books from 100 countries and try to determine the level of regional interest. We will examine the preference of global readers in a systematic manner. Based on the ten most downloaded books from 100 countries during a 12 month period , the focus on regional topics will be measured in two ways: the amount of books written in non-English languages, and the amount of English language books that mention the country.
DOI: 10.34740/kaggle/dsv/3372239
title: Mahanagar Yatayat Dataset
DOI: 10.6084/m9.figshare.19433018
title: Increased microbial and substrate complexity result in higher molecular diversity of the dissolved organic matter (DOM) pool

description: Marine dissolved organic matter (DOM) represents one of the largest reduced carbon pools on Earth. Diverse microbial metabolic activities play important roles in shaping this pool. But the interactions between the complexity of DOM substrates and microbial communities and its influence on the microbial produced DOM remain poorly understood. In this study, we investigated how the addition of substrates with different complexity (glucose, laminarin and Synechococcus-derived DOM) impacted the production of new DOM by an individual opportunist Alteromonas strain and a coastal natural microbial community. Our experiments reveal that the microbial complexity was more important than the substrate in shaping the molecular composition of DOM. Further, our results showed that the DOM composition played a larger role in shaping the microbial compositions than the concentrations of DOM and inorganic nutrients. Combined our network analysis revealed that complex substrates resulted in higher molecular diversity of the microbial produced DOM, sustained a higher microbial diversity, and maintained a more diverse association between microbial communities and DOM molecules. Overall, the present study indicate that the microbial diversity contributes to DOM molecular diversity and vice versa, highlighting the importance of microbial-substrate interactions in shaping the marine DOM pool.
DOI: 10.6084/m9.figshare.19433018.v1
title: Increased microbial and substrate complexity result in higher molecular diversity of the dissolved organic matter (DOM) pool

description: Marine dissolved organic matter (DOM) represents one of the largest reduced carbon pools on Earth. Diverse microbial metabolic activities play important roles in shaping this pool. But the interactions between the complexity of DOM substrates and microbial communities and its influence on the microbial produced DOM remain poorly understood. In this study, we investigated how the addition of substrates with different complexity (glucose, laminarin and Synechococcus-derived DOM) impacted the production of new DOM by an individual opportunist Alteromonas strain and a coastal natural microbial community. Our experiments reveal that the microbial complexity was more important than the substrate in shaping the molecular composition of DOM. Further, our results showed that the DOM composition played a larger role in shaping the microbial compositions than the concentrations of DOM and inorganic nutrients. Combined our network analysis revealed that complex substrates resulted in higher molecular diversity of the microbial produced DOM, sustained a higher microbial diversity, and maintained a more diverse association between microbial communities and DOM molecules. Overall, the present study indicate that the microbial diversity contributes to DOM molecular diversity and vice versa, highlighting the importance of microbial-substrate interactions in shaping the marine DOM pool.
DOI: 10.6084/m9.figshare.19435649
title: Determinant of Success for Collaborative Project Management - Interview Questions

description: Interview questions used to guide the interviewer in extracting the background of the interviewer, issue in the project and organization, success criteria of completed projects and perceived determinants of success factor for past projects. The interviewer did not use the exact words or structure of sentences as enclosed; adjustments were made during the interview as per relevancy and continuity of the discussion.
DOI: 10.6084/m9.figshare.19435649.v1
title: Determinant of Success for Collaborative Project Management - Interview Questions

description: Interview questions used to guide the interviewer in extracting the background of the interviewer, issue in the project and organization, success criteria of completed projects and perceived determinants of success factor for past projects. The interviewer did not use the exact words or structure of sentences as enclosed; adjustments were made during the interview as per relevancy and continuity of the discussion.
DOI: 10.5517/ccdc.csd.cc1nxxdw
title: CCDC 1547129: Experimental Crystal Structure Determination

description: Related Article: Manisha Mishra, Dylan Twardy, Clifford Ellstrom, Kraig A. Wheeler, Roman Dembinski, Béla Török|2019|Green Chemistry|21|99|doi:10.1039/C8GC02520G
DOI: 10.5517/ccdc.csd.cc1ytbps
title: CCDC 1811816: Experimental Crystal Structure Determination

description: Related Article: Manisha Mishra, Dylan Twardy, Clifford Ellstrom, Kraig A. Wheeler, Roman Dembinski, Béla Török|2019|Green Chemistry|21|99|doi:10.1039/C8GC02520G
DOI: 10.5517/ccdc.csd.cc27g989
title: CCDC 2069320: Experimental Crystal Structure Determination

description: Related Article: Chun-Hong Li, Jie-Hui Yu, Ji-Qing Xu|2022|J.Solid State Chem.|306|122748|doi:10.1016/j.jssc.2021.122748
DOI: 10.5517/ccdc.csd.cc27g9bc
title: CCDC 2069322: Experimental Crystal Structure Determination

description: Related Article: Chun-Hong Li, Jie-Hui Yu, Ji-Qing Xu|2022|J.Solid State Chem.|306|122748|doi:10.1016/j.jssc.2021.122748
DOI: 10.5517/ccdc.csd.cc28wd3p
title: CCDC 2111692: Experimental Crystal Structure Determination

description: Related Article: Li-Long Dang, Ting-Ting Li, Chen-Chen Zhao, Ting-Ting Zhang, Xiao-Yu Ye, Xiao-Tian Sun, Hua-Rui Wang, Lu-Fang Ma|2022|J.Solid State Chem.|306|122785|doi:10.1016/j.jssc.2021.122785
DOI: 10.5517/ccdc.csd.cc28x75m
title: CCDC 2112500: Experimental Crystal Structure Determination

description: Related Article: Xiao-Nan Lv, Yu-Hang Zhang, Ping-Ping Sun, Peng-Fei Wang, Jun-Jie Tang, Gang Yang, Qi Shi, Fa-Nian Shi|2022|J.Solid State Chem.|306|122786|doi:10.1016/j.jssc.2021.122786
DOI: 10.5517/ccdc.csd.cc28x6jy
title: CCDC 2112480: Experimental Crystal Structure Determination

description: Related Article: Xiao-Nan Lv, Yu-Hang Zhang, Ping-Ping Sun, Peng-Fei Wang, Jun-Jie Tang, Gang Yang, Qi Shi, Fa-Nian Shi|2022|J.Solid State Chem.|306|122786|doi:10.1016/j.jssc.2021.122786
DOI: 10.5517/ccdc.csd.cc28x74l
title: CCDC 2112499: Experimental Crystal Structure Determination

description: Related Article: Xiao-Nan Lv, Yu-Hang Zhang, Ping-Ping Sun, Peng-Fei Wang, Jun-Jie Tang, Gang Yang, Qi Shi, Fa-Nian Shi|2022|J.Solid State Chem.|306|122786|doi:10.1016/j.jssc.2021.122786