cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10429230
title: Intermittent Lobe Reconnection under Prolonged Northward Interplanetary Magnetic Field Condition:  Insights from Cusp Spot Event Observations

description: SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America. We would like to thank British Antarctic Survey (https://www.bas.ac.uk/project/superdarn) and the University of Saskatchewanan (https://superdarn.ca) for hosting the SuperDARN data mirrors access. The EISCAT dataset is available from the Madrigal database (http://millstonehill.haystack.mit.edu). We acknowledge the use of DMSP/SSUSI data provided by the Johns Hopkins University Applied Physics Laboratory (https://cdaweb.gsfc.nasa.gov). Additionally, the OMNI dataset is available from the OMNIWeb service online of NASA/GSFC's Space Physics Data Facility's (https://spdf.gsfc.nasa.gov/pub/data/omni/).
DOI: 10.5281/zenodo.7092358
title: CoCO2-MOSAIC 1.0: a global mosaic of regional, gridded, fossil and biofuel CO2 emission inventories

description: CoCO2-MOSAIC 1.0 is a global mosaic of regional bottom-up inventories of anthropogenic CO2 emissions developed in the framework of the CoCO2 project (https://coco2-project.eu/). CoCO2-MOSAIC 1.0 provides gridded (0.1˚×0.1˚) monthly emissions fluxes of CO2 fossil fuel (CO2ff, long cycle) and CO2 biofuel (CO2bf, short cycle) for the years 2015 to 2018 disaggregated in seven sectors: energy_s (super-emitting sources above 7.9e-6 kg/m2/s), energy_a (average emitters), manufacturing, settlements, transport, aviation land/take-off (LTO) and other. The regional inventories included are CAMS-GHG-REG 5.1 (Europe), DACCIWA 2.0 (Africa), GEAA-AEI 3.0 (Argentina), INEMA 1.0 (Chile), REAS 3.2.1 (South-East Asia) and VULCAN 3.0 (USA). EDGAR 6.0 and CAMS-GLOB-SHIP 3.1 are used for gap-filling missing sectors and regions. CAMS-GLOB-TEMPO 3.1 is used for temporal disaggregation of inventories providing annual emissions. Aviation emissions from climb, descent, and cruise are not covered by regional inventories and are provided as a separate file. Note that 2015 is the only year when all regional inventories are simultaneously available. Compared to global inventories, CoCO2-MOSAIC 1.0 includes all the regional information available without the limitation of providing spatially consistent emissions. Therefore, CoCO2-MOSAIC 1.0 can be used as a global baseline inventory due to the higher level of detail, higher spatial resolution, and country-specific information included by regional inventories. For further details see Urraca et al. 2023 (ESSD submitted). The paper (i) describes the CoCO2-MOSAIC methodology and (ii) uses the mosaic to inter-compare the most widely used global inventories: CAMS-GLOB-ANT 5.3, EDGAR 6.0/7.0, ODIAC v2020b, and CEDS v2020_04_24.
description: {"references": ["Urraca R, Jansens-Maenhout G, Denier Van der Gon H, Dellaert S, Kuenen J, Visschedijk A, Granier C, Soulie A, Keita S, Darras S, Guevara M, Puliafito E, Lopez-Nore\u00f1a A, Berna-Pe\u00f1a L, Huneeus N, Alamos N, Gurney K, Roest G, Grassi G, Rossi S, Gobron N, Dowell M. CoCO2-MOSAIC 1.0: a global mosaic of regional, gridded, fossil and biofuel CO2 emission inventories. ESSD (submitted), 2023"]}
DOI: 10.5281/zenodo.7625135
title: Dataset for the "a parameterization for cloud organization and propagation by  evaporation-driven cold pools edges"

description: When the negatively buoyant air in the cloud downdrafts reaches the surface, it spreads out horizontally, producing cold pools. A cold pool can trigger new convective cells. However, when combined with the ambient vertical wind shear, it can also connect and upscale them into large mesoscale convective systems (MCS). Given the broad spectrum of scales of the atmospheric phenomenon involving the interaction between cold pools and the MCS, a parameterization was designed here. Then, it is coupled with a classical convection parameterization to be applied in an atmospheric model with an insufficient spatial resolution to explicitly resolve convection and the sub-cloud layer. A new scalar quantity related to the deficit of moist static energy detrained by the downdrafts mass flux is proposed. This quantity is subject to grid-scale advection, mixing, and a sink term representing dissipation processes. The model is then applied to simulate moist convection development over a large portion of tropical land in the Amazon Basin in a wet and dry-to-wet 10-days period. Our results show that the cold pool edge parameterization improves the organization, longevity, propagation, and severity of simulated MCS over the Amazon and other different continental areas.
 
description: ----
DOI: 10.6084/m9.figshare.21710468
title: Data package for the Nature Astronomy paper: "Salts and organics on Ganymede’s surface from infrared observations by Juno/JIRAM" (Tosi et al.)

description: The file named: "JIRAM_global_average_spectrum.txt" is an ASCII file containing the spectral profile shown in Figure 1b (global average spectrum of Ganymede acquired by JIRAM in sequence PJ34 on 7 June 2021). The first column represents the JIRAM set of wavelengths expressed in microns (µm), while the other columns are respectively the I/F value and the associated standard error of the mean (SEM) value, also expressed in units of I/F. The file named: "JIRAM_spectral_slits.txt" is an ASCII file containing the spectral profiles shown in Figure 2 (average spectral profiles of slits 1 to 5). The first column represents the JIRAM set of wavelengths expressed in microns (µm), while the other columns are respectively the I/F value and the associated standard error of the mean (SEM) value, also expressed in units of I/F. The file named: "Band_depths_Ganymede_JM0340_4paper.txt" is an ASCII file containing band depth values computed for slits 1 to 5, as shown in Figure 3. For each JIRAM pixel/spectrum, geometric information computed with SPICE is also provided. The files named: "JunoCam_slit1_Greenfilter.tif", "JunoCam_slit2_Greenfilter.tif" and "JunoCam_slit3_Greenfilter.tif", are TIFF images of Ganymede containing the optical data acquired by the JunoCam camera in its green filter during the same flyby and corresponding to the areas covered by the JIRAM data relating to slit 1, 2 and 3. These images were used as background layer in Figure 3 and in Supplementary Figure 14 for the three columns relating to these slits. These images display a better pixel resolution than the USGS Galileo+Voyager basemap used as background for slits 4 and 5. The file named: "Extended_Data_Table_NatAstron_Tosi.xlsx" is the fully accessible Excel spreadsheet presented as Extended Data Table 1. The meaning of the rows and columns in the spreadsheet are specified in the caption found in the article. <br> .v2 version history: A corrected version of Extended_Data_Table_NatAstron_Tosi.xlsx has been provided (updated at row 6) to align with the published version at: https://doi.org/10.1038/s41550-023-02107-5
DOI: 10.5281/zenodo.6395539
title: Silva 138.1 taxonomy classifiers for use with QIIME 2 q2-feature-classifier

description: Uniform and weighted naive Bayes classifiers trained on Silva 138.1 data for use with QIIME 2 q2-feature-classifier. full-length-average-classifier.qza and 515f-806r-average-classifier.qza are classifiers using weights averaged across 14 EMPO 3 habitat types. If in doubt, use one of these. Original weights derived from Qiita, scripts used to derive them, and additional information available at https://github.com/BenKaehler/readytowear. Classifiers trained on full-length 16S or 515F/806R region as labelled. Full length Silva 138.1 reference sequences and corresponding taxonomies are in ref-seqs.qza an ref-tax.qza. If you use any of the weighted classifiers, please cite Kaehler BD, Bokulich NA, McDonald D, Knight R, Caporaso JG, Huttley GA. (2019). Species-level microbial sequence classification is improved by source-environment information. Nature Communications 10: 4643. doi: https://doi.org/10.1038/s41467-019-12669-6 If you use the any of the classifiers (weighted or otherwise), please cite Bokulich, N.A., Kaehler, B.D., Rideout, J.R. et al. (2018). Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6, 90. doi: https://doi.org/10.1186/s40168-018-0470-z If you use any file from here, please cite: Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glöckner FO (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. <em>Nucl. Acids Res</em>. 41 (D1): D590-D596 Robeson, M. S., O’Rourke, D. R., Kaehler, B. D., Ziemski, M., Dillon, M. R., Foster, J. T., & Bokulich, N. A. (2021). RESCRIPt: Reproducible sequence taxonomy reference database management. <em>PLoS Comp. Bio.</em>, <em>17</em>(11). doi: https://doi.org/10.1371/journal.pcbi.1009581 Warning: Pre-trained classifiers that can be used with q2-feature-classifier currently present a security risk. If using a pre-trained classifier such as the ones provided here, you should trust the person who trained the classifier and the person who provided you with the qza file.
description: {"references": ["Kaehler BD, Bokulich NA, McDonald D, Knight R, Caporaso JG, Huttley GA. (2019). Species-level microbial sequence classification is improved by source-environment information. Nature Communications 10: 4643.", "Bokulich, N.A., Kaehler, B.D., Rideout, J.R. et al. (2018). Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2's q2-feature-classifier plugin. Microbiome 6, 90.", "Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Gl\u00f6ckner FO (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucl. Acids Res. 41 (D1): D590-D596", "Robeson, M. S., O'Rourke, D. R., Kaehler, B. D., Ziemski, M., Dillon, M. R., Foster, J. T., & Bokulich, N. A. (2021). RESCRIPt: Reproducible sequence taxonomy reference database management.\u00a0PLoS Comp. Bio.,\u00a017(11)."]}
DOI: 10.5281/zenodo.889798
title: Crowdsourced Wifi Database And Benchmark Software For Indoor Positioning

description: This dataset contains two Wi-Fi databases (one for training and one for test/estimation purposes in indoor positioning applications), collected in a crowdsourced mode (i.e., via 21 different devices and different users), together with a benchmarking utility software (in Matlab and Python) to illustrate various algorithms of indoor positioning based solely on WiFi information (MAC addresses and RSS values). 

The data was collected in a 4-floor university building in Tampere, Finland,  during Jan-Aug 2017 and it comprises 687 training fingerprints and 3951 test or estimation fingerprints.

The dataset and/or the associated software are to be cited as follows:

E.S. Lohan, J. Torres-Sospedra, P. Richter, H. Leppäkoski, J. Huerta, A. Cramariuc, “Crowdsourced WiFi-fingerprinting database and benchmark software for indoor positioning”, Zenodo repository, DOI 10.5281/zenodo.889798

A detailed description of our data can be found here:

Lohan, Elena Simona, Torres-Sospedra, Joaquín, Leppäkoski, Helena, Richter, Philipp, Peng, Zhe, Huerta, Joaquín<br>
"Wi-Fi Crowdsourced Fingerprinting Dataset for Indoor Positioning", MDPI Data journal, 16 pages, doi:10.3390/data2040032, http://www.mdpi.com/2306-5729/2/4/32
DOI: 10.6084/m9.figshare.24188430
title: Differences in spatial niche of terrestrial mammals when facing extreme snowfall: the case in east Asian forests

description: Tracks of seven mammal species (wild boar [<i>Sus scrofa]</i>, Japanese serow [<i>Capricornis crispus</i>]), red fox [<i>Vulpes vulpes</i>], raccoon dog [<i>Nyctereutes procyonoides</i>], Japanese marten [<i>Martes melampus</i>]), Japanese hare [<i>Lepus brachyuru</i><i>s</i>]), Japanese macaque [<i>Macaca fuscata</i>]) left on the snow surface in eastern Japan from 2015 to 2020.Methods:We set 55 survey transects in low-mountain forests with mosaic landscapes below 800 m above sea level within the Towada, Asahi, and Oguni regions, located in northern Japan. In most areas in these regions, maximum snowfall with &gt; 2 m in depth was normally observed. The total length of the transects was 21 km in Towada, 180 km in Asahi, and 111 km in Oguni. We followed the transects on skis during the daytime when snowfall, which obliterated mammal tracks from the snow surface, had not been observed for &gt; 24 hours before each survey. Then, we recorded a geolocation of distinct tracks only when those intersected transects. We conducted this survey once a mid-winter from 2016 to 2020 in Towada, from 2014 to 2019 in Asahi, and from 2015 to 2019 in Oguni. Although we could not follow all the transects every year because of limitations due to weather conditions, the total length of the transects that we explored reached 1,144 km. The mean completion percentage of transects surveyed each year was 90% in Towada, 52% in Asahi, and 87% in Oguni.
DOI: 10.6084/m9.figshare.24188430.v1
title: Differences in spatial niche of terrestrial mammals when facing extreme snowfall: the case in east Asian forests

description: Tracks of seven mammal species (wild boar [<i>Sus scrofa]</i>, Japanese serow [<i>Capricornis crispus</i>]), red fox [<i>Vulpes vulpes</i>], raccoon dog [<i>Nyctereutes procyonoides</i>], Japanese marten [<i>Martes melampus</i>]), Japanese hare [<i>Lepus brachyuru</i><i>s</i>]), Japanese macaque [<i>Macaca fuscata</i>]) left on the snow surface in eastern Japan from 2015 to 2020.Methods:We set 55 survey transects in low-mountain forests with mosaic landscapes below 800 m above sea level within the Towada, Asahi, and Oguni regions, located in northern Japan. In most areas in these regions, maximum snowfall with &gt; 2 m in depth was normally observed. The total length of the transects was 21 km in Towada, 180 km in Asahi, and 111 km in Oguni. We followed the transects on skis during the daytime when snowfall, which obliterated mammal tracks from the snow surface, had not been observed for &gt; 24 hours before each survey. Then, we recorded a geolocation of distinct tracks only when those intersected transects. We conducted this survey once a mid-winter from 2016 to 2020 in Towada, from 2014 to 2019 in Asahi, and from 2015 to 2019 in Oguni. Although we could not follow all the transects every year because of limitations due to weather conditions, the total length of the transects that we explored reached 1,144 km. The mean completion percentage of transects surveyed each year was 90% in Towada, 52% in Asahi, and 87% in Oguni.
DOI: 10.22008/fk2/xblerl
title: Geologisk kort over Danmark, 1:200 000. Kortbladet Femer Bælt - Arkona Bassinet. Sen-kvartære aflejringer

description: Weichselian, Holocene, Baltic Sea, Baltic Ice Lake, marine geology,
DOI: 10.5281/zenodo.10554505
title: CS-IAAS
DOI: 10.5281/zenodo.10393478
title: CS-IAAS
DOI: 10.5281/zenodo.10393479
title: CS-IAAS
DOI: 10.6084/m9.figshare.25046183
title: dados.xltx

description: Plant abundance data in areas under different fire frequencies in floodable and flood-free environments in the Kadiweu Indigenous Territory, MS, Brazil. In the spreadsheet we present data grouped by buffers and landscape metrics regarding fire frequency patterns. Additionally, we provide abundance data in sample plots in fire frequency classes: high, low and moderate. This dataset includes data for the total environment (floodable + flood-free areas) and partitioned into floodable and flood-free areas.
DOI: 10.5281/zenodo.10554518
title: Paleoceanographic data on core M17730-4

description: Paleoceanographic data on core M17730-4 (72°06.70'N, 07°23.30'E, 2749 m water depth):

M17730-4-age_determination.csv

Age determination on core M17730-4

M17730-4-planktic_foraminifera+SST.csv

Relative abundances of planktic foraminiferal species [%], total abundance of planktic foraminifera [#/g], and summer and winter SST at 100 m water depth [°C].
DOI: 10.5281/zenodo.10554519
title: Paleoceanographic data on core M17730-4

description: Paleoceanographic data on core M17730-4 (72°06.70'N, 07°23.30'E, 2749 m water depth):

M17730-4-age_determination.csv

Age determination on core M17730-4

M17730-4-planktic_foraminifera+SST.csv

Relative abundances of planktic foraminiferal species [%], total abundance of planktic foraminifera [#/g], and summer and winter SST at 100 m water depth [°C].
DOI: 10.5517/ccdc.csd.cc2hblfw
title: CCDC 2304088: Experimental Crystal Structure Determination

description: Related Article: Nusik Gedikoglu, Jon Ostolaza-Paraiso, Elodie Grange, Michael Paris, Stéphane Grolleau, David Fairen-Jimenez, Thomas Devic|2024|Microporous and Mesoporous Materials|367|112968|doi:10.1016/j.micromeso.2023.112968
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: 10.6084/m9.figshare.25037648
title: Co-occurring invasive plant and propagule pressure accelerate invasion, but only multiple species invasion changes ecosystem multifunctionality

description: The understanding of the interactions between individual invasive plant species in biological invasion is currently limited. Specifically, the impact of both invasive plant richness (interaction between invasive species) and abundance (propagule pressure) is likely to be crucial in determining invasion outcomes and their potential effects on ecosystem functions and multifunctionality. Here, we conducted a mesocosm experiment to assess the effects of different levels of invasive plant richness and abundance on invasion success, community properties, ecosystem functions, and multifunctionality. We show that both invasive plant richness and abundance promoted invasion success, primarily due to the allelopathic effects on native plants. We also observed invasive plant richness had different effects on ecosystem functions, and an initial increase followed by a subsequent decrease in ecosystem multifunctionality as invasive plant richness increased, with no significant impact from invasive plant abundance. This suggests that the interaction between invasive plants plays a more important role in the impacts on ecosystem functions and multifunctionality than propagule pressure. Furthermore, plant invasion indirectly affected multifunctionality by impacting species diversity indices and environmental factors. These results highlight the potential for aggravated biological invasion and intensified ecosystem damage in the future as multiple invasive species co-invade.
DOI: 10.4121/97ce0a99-bdaa-4fbf-b548-f6d85c017915.v1
title: Frequency and Time Response Data presented in the publication: The Influence of Floating Turbine Dynamics on the Helix Wake Mixing Method

description: Dear user,<br>This data repository contains all the information used in the linked Torque 2024 paper. There are three folders,each containing data corresponding to one part of the results presented in the paper. Each folder contains the data in a .mat format and a script that opens this data set and processes it to recreate the same images used in the paper.<br>-- Frequency IdentificationThe folder 'Frequency_Identification_Data' contains the input-output data used to create the frequency response functions (frf). The data includes, among the 4 turbines analyzed, the responses for the OC4 floating turbine, the IEA 15MW turbine and the Hexafloat turbine. Line 10 in the Matlab code indicates which entry corresponds to which turbine. The frfs are created using a system identification toolbox developed at the TU Delft. These functions are included in the Functions subfolder located in the Frequency_Identification_Data folder. The system responses are created in lines 41-51 and look like:<br>[G_Surge,sys_Surge,~] = id_funcs(detrend(squeeze(PitchYaw)),detrend(squeeze(Turbine1_data(:,12))),dt,4,6) ;<br>This line of code uses the Matlab function id_funcs to calculate the input-output spectrum between the blade pitch signals and the floating turbine surge motion. This spectrum is contained in the sys_Surge variable. G_surge contains a state space model that is fitted to this spectrum. The last three inputs are the sampling time, resampling and model order. The last two are only required for model fitting. The contents of the id_funcs are taken from: https://www.dcsc.tudelft.nl/~jwvanwingerden/pbsid/pbsidtoolbox_product_page.html. The Hexafloat experiment was done at a later stage and added afterwards, hence it has its own for-loop for identification. After identification has been done (this can take some time), the same figures as those shown in the manuscript are created.<br>-- Time Domain Data.The time domain data used in the paper can be found in the 'Prescribed_Motion_Data' and 'Time_Domain_Data'. Both folder contains a .mat data file containing the wind speeds measured at the locations mentioned in each case's work and a Matlab script to analyze this data and create the figures as shown in the work. The function crameri.m together with the CrameriColourMaps7.0.mat is required to get the same colour maps. These colourmaps are designed to represent the data best, for more information see: https://www.fabiocrameri.ch/colourmaps/.<br>All of the scripts were tested in MATLAB R2022b.
DOI: 10.6084/m9.figshare.22598236
title: Data associated with the manuscript: Can glacial sea-level drop-induced gas hydrate dissociation cause submarine landslides?

description: Data associated with the manuscript: Can glacial sea-level drop-induced gas hydrate dissociation cause submarine landslides?
DOI: 10.6084/m9.figshare.22598236.v3
title: glacial hydrate dissociation and submarine landslide

description: This is the archived data for the manuscript <i>Can glacial sea-level drop-induced gas hydrate dissociation cause submarine landslides?</i>
DOI: 10.6084/m9.figshare.25046228
title: Supplementary Material for: Association Between Pre-Operative Total Prostate Specific Antigen and Survivorship of Prostate Cancer Following Radical Prostatectomy: A Systematic Review

description: Objective: This review aimed to systematically quantify the association between pre-operative total prostate specific antigen (tPSA) and survivorship of prostate cancer (PCa). Methods: Data sources for the review included MEDLINE, PubMed, Cochrane Library, CINAHL, Academic Search Complete, PsycINFO, and relevant reference lists. Databases were searched from inception to June, 2022. The study took place between May 2022 and March 2023. We included studies that applied a quantitative approach to examine the interaction between pre-operative PSA and survivorship of PCa. Pre-operative PSA constituted the independent variable, whereas survivorship of prostate cancer as measured by biochemical recurrence and mortality constitute the outcome variable. A risk of bias assessment was conducted with the aid of a mixed-method appraisal tool. We employed meta-analysis to quantify the association of pre-operative PSA with biochemical recurrence and mortality and computed I2 to assess the degree of heterogeneity. Results: We found a positive weak association between pre-operative PSA and biochemical recurrence (HR = 1.074; 95% CI = 1.042 - 1.106). With a median rise in PSA (≥ 2 ng/mL), the likelihood for biochemical recurrence increase by approximately 7.4%. There was statistically a significant association between PSA and mortality (HR = 1.222, CI 0.917 - 1.630). Conclusions: Biochemical recurrence associates with pre-operative PSA in an inconsistent manner. The sole use of pre-operative PSA in estimating post-prostatectomy biochemical recurrence should be discouraged. There is need for a multi-factorial model which employs a prudent combination of the most important and cost-effective biomarkers in predicting post-prostatectomy biochemical recurrence.