cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.838970
title: East-West And Vertical Deformation Maps Of Alto Tiberina Fault Supersite: The Post-Proc Service  In Geohazard Exploitation Platform Applied To Sentinel-1 Dataset

description: In the framework of the ESA funded project “MEMpHIS - Multi Scale and Multi Hazard Mapping Space based Solutions ”, the Istituto Nazionale di Geofisica e Vulcanologia (INGV) together with TRE-ALTAMIRA, generated the EAST-WEST and VERTICAL deformation maps of Alto Tiberina Fault supersite. The two maps of ground velocity were derived thanks to the adoption of a specific tool named "Post-Proc", implemented in MEMPHIS and with the support of Terradue, that is  able to automatically re-project on the east-west and vertical directions the ascending and descending InSAR time series. In particular, the output refers to the deformation maps calculated by processing with SqueeSAR (TM) method, a large dataset acquired by the ESA Sentinel-1 mission.

The Post-Proc tool also calculates the mean accelerations associated to each persistent scatterer in the scenes. Some additional features are also available from this tool:

-          Change the coherence threshold for selecting a subset of persistent scatterers

-          Activate a geometrical distortion filter to take into account the layover and foreshortening effects

-          Change the reference point (position and coherence)

-          Choose between two types of accelerations:  2<sup>nd</sup> order model or velocity derivative

-          Choose among three different projection: east-west, vertical, and downslope.

The present dataset is composed of the EAST-WEST and VERTICAL acceleration maps. The data are in shapefile format: for each record (PS) the topography, velocity, acceleration, and InSAR coherence is reported.
DOI: 10.5281/zenodo.1197250
title: Research On Green Total-Factor Productivity Of Water Resources And Its Influencing Factors In China

description: These files include the data used to calculate green total-factor productivity of water resources and data of its certain influencing factors.
DOI: 10.1594/pangaea.846695
title: Pollen and non-pollen palynomorphs counting data from Borsteler Moor (Germany)

description: In order to reconstruct regional vegetation changes and local conditions during the fen-bog transition in the Borsteler Moor (northwestern Germany), a sediment core covering the period between 7.1 and 4.5 cal kyrs BP was palynologically in vestigated. The pollen diagram demonstrates the dominance of oak forests and a gradual replacement of trees by raised bog vegetation with the wetter conditions in the Late Atlantic. At ~ 6 cal kyrs BP, the non-pollen palynomorphs (NPP) demonstrate the succession from mesotrophic conditions, clearly indicated by a number of fungal spore types, to oligotrophic conditions, indicated by Sphagnum spores, Bryophytomyces sphagni, and testate amoebae Amphitrema, Assulina and Arcella, etc. Four relatively dry phases during the transition from fen to bog are clearly indicated by the dominance of Calluna and associated fungi as well as by the increase of microcharcoal. Several new NPP types are described and known NPP types are identified. All NPP are discussed in the context of their palaeoecological indicator values.
description: BM, HdV and IBB are abbreviation for non-pollen palynomorphs types: BM = Borsteler Moor (new types that we describe for the first time in our paper), HdV = Hugo de Vries Laboratory, University of Amsterdam (the Netherlands) for types described in this lab, IBB = Palynology and Palaeoecology Lab, Botanical Institute of Barselona (Spain) for types described in this lab.
description: Supplement to: Shumilovskikh, Lyudmila S; Schlütz, Frank; Achterberg, Inke E M; Bauerochse, Andreas; Leuschner, Hanns Hubert (2015): Non-pollen palynomorphs from mid-Holocene peat of the raised bog Borsteler Moor (Lower Saxony, Germany). Studia Quaternaria, 32(1), 5-18
DOI: 10.5281/zenodo.5031815
title: Spatial–temporal dynamics of gesture–speech integration: a simultaneous EEG-fMRI study

description: The semantic integration between gesture and speech (GSI) is mediated by the left posterior temporal sulcus/middle temporal gyrus (pSTS/MTG) and the left inferior frontal gyrus (IFG). Evidence from electroencephalography (EEG) suggests that oscillations in the alpha and beta bands may support processes at different stages of GSI. In the present study, we investigated the relationship between electrophysiological oscillations and blood-oxygen-level-dependent (BOLD) activity during GSI. In a simultaneous EEG-fMRI study, German participants (n = 19) were presented with videos of an actor either performing meaningful gestures in the context of a comprehensible German (GG) or incomprehensible Russian sentence (GR), or just speaking a German sentence (SG). EEG results revealed reduced alpha and beta power for the GG vs. SG conditions, while fMRI analyses showed BOLD increase in the left pSTS/MTG for GG &gt; GR ∩ GG &gt; SG. In time-window-based EEG-informed fMRI analyses, we further found a positive correlation between single-trial alpha power and BOLD signal in the left pSTS/MTG, the left IFG, and several sub-cortical regions. Moreover, the alpha-pSTS/MTG correlation was observed in an earlier time window in comparison to the alpha-IFG correlation, thus supporting a two-stage processing model of GSI. Our study shows that EEG-informed fMRI implies multiple roles of alpha oscillations during GSI, and that the method is a best candidate for multidimensional investigations on complex cognitive functions such as GSI.
DOI: 10.1594/pangaea.848754
title: XRD mineralogy of sediment core PS2631-5

description: We employ the quantitative X-ray diffraction method developed by Eberl (2003, 2004). The sediment &lt; 2 mm are spiked by 10% by weight of zincite, milled, and the samples are scanned on a D5000 Siemens X-Ray Diffractometer between 5° and 65° 2-theta with a 0.02° step and a 2-sec count. The resulting 3000 data points are loaded into the Excel macro-program Rockjock v6 (Eberl, 2003) and the weight% of minerals in the sediment mixtures calculated against a suite of 124 "standard" mineral patterns (Eberl, 2003). Finally, the data are normalized to sum to 100%
DOI: 10.5281/zenodo.5636044
title: Data and plotting code for 'Many-body-localized discrete time crystal with a programmable spin-based quantum simulator'

description: Underlying data and plotting for the publication: "Many-body-localized discrete time crystal with a programmable spin-based quantum simulator", J. Randall et al., 2021. This directory contains: - “Data_analysis.ipynb”: The master Jupyter notebook from which all plots produced in the main text and supplementary materials can be reproduced.<br> - “Data.zip”: A zipped folder containing the json files as loaded within “Data_analysis.ipynb”. These files contain the underlying x, y, and, where appropriate, y error values for each corresponding plot. The notebook should be executed using python3.
DOI: 10.5281/zenodo.5565193
title: Garbage Dataset

description: 1000 images of randomly generated trashes!
DOI: 10.5281/zenodo.5666302
title: 青藏高原湖泊水位水量年变化速率

description: 2000-2020年青藏高原10 km2以上湖泊水位水量年变化速率
DOI: 10.5281/zenodo.6024550
title: In-situ lake level observations on the western Tibetan Plateau

description: Lake level has been monitored at six lakes (Lumajiangdong Co, Memar Co, Jieze Caka, Longmu Co, Luotuo Lake, Aksaiqin Lake) on the western TP. HOBO water level loggers (U20-001-01) or Solist water level loggers were installed in the littoral zone of the lakes. To validate lake level changes, two HOBO water-level loggers were installed in different regions of Lumajiangdong Co (Fig. S1). Because water levels were recorded as changes in pressure (less than 0.5 cm water level equivalent), air pressure data were subtracted from the level loggers to obtain pressure changes related to water column variations. The loggers can still work efficiently at the beginning of ice cover. However, there is no data available when the lake ice was thick enough and loggers were frozen up. Daily lake-level changes were used in this study. At Lumajiangdong Co, lake level data are available between October 2016 and September 2021. At Memar Co, lake level data are only available between October 2017 and September 2019 and between October 2020 and September 2021. For Jieze Caka, Longmu Co and Luotuo Lake, there is only one year’s data available. Water depth of the loggers was measured during fieldwork to validate the logger data.
DOI: 10.5281/zenodo.6474685
title: In-situ lake level observations on the western Tibetan Plateau

description: Lake level has been monitored at six lakes (Lumajiangdong Co, Memar Co, Jieze Caka, Longmu Co, Luotuo Lake, Aksaiqin Lake) on the western TP. HOBO water level loggers (U20-001-01) or Solist water level loggers were installed in the littoral zone of the lakes. To validate lake level changes, two HOBO water-level loggers were installed in different regions of Lumajiangdong Co (Fig. S1). Because water levels were recorded as changes in pressure (less than 0.5 cm water level equivalent), air pressure data were subtracted from the level loggers to obtain pressure changes related to water column variations. The loggers can still work efficiently at the beginning of ice cover. However, there is no data available when the lake ice was thick enough and loggers were frozen up. Daily lake-level changes were used in this study. At Lumajiangdong Co, lake level data are available between October 2016 and September 2021. At Memar Co, lake level data are only available between October 2017 and September 2019 and between October 2020 and September 2021. For Jieze Caka, Longmu Co and Luotuo Lake, there is only one year’s data available. Water depth of the loggers was measured during fieldwork to validate the logger data.
DOI: 10.5281/zenodo.4719835
title: WPuQ

description: Electric load of 38 households measured in a small village in Lower Saxony, Germany. Data is available for voltage, reactive power and active power. Seperate measurements are available for each total household load, for each heat pump and at the local power transformer. Different temporal and spatial aggregations are available for conveniance.
DOI: 10.5281/zenodo.5484304
title: TROPOMI-derived ground level NO2 concentrations (2019 Annual mean)

description: Annual mean ground level NO2 concentrations derived from TROPOMI satellite NO<sub>2</sub> observations. Ground level concentrations are derived from observed column densities using the GEOS-Chem chemical transport model constrained with ground monitor observations following the method outlined in Cooper et al 2021 (DOI: 10.1038/s41586-021-04229-0) <br> Annual mean data are provided at ~1x1 km<sup>2</sup> resolution at satellite overpass time (~1:30 PM local). Datasets are in netcdf (.nc) format.
DOI: 10.5281/zenodo.3905102
title: The relationship between EEG and fMRI connectomes is reproducible across simultaneous EEG-fMRI studies from 1.5T to 7T

description: <strong>Connectome dataset for the publication: 'The relationship between EEG and fMRI connectomes is reproducible across simultaneous EEG-fMRI studies from 1.5T to 7T'<br> Wirsich et al. 2021, NeuroImage, doi: </strong>10.1016/j.neuroimage.2021.117864 <strong>Data description</strong> <em>eeg-fmri_connectomes_$atlas$_scrubbed_$dataset$.mat</em><br> datasets with filename truncTo4min58_5s hold static connectivities based on timeseries truncated<br> to 4min58.5s. All other datasets are based on static connectivities derived from the total session<br> timeseries. <br> subj: subject<br> subj.name: name of the subject<br> subj.sess: session<br> subj.sess.sess_name: name of the session<br> subj.sess.fMRI: vector of upper triangular of fMRI connectivity<br> subj.sess.EEG: EEG connectomes<br> subj.sess.EEG.name: name of connectivity measure used (imaginary part of the coherency: iCoh,<br> amplitude envelope correlation (orthongonaliyzed): hilb_Ortho, Amplitude envelope correlation<br> (not orthongonaliyzed): hilb_noOrtho)<br> subj.sess.EEG.bands: EEG frequency bands<br> subj.sess.EEG.bands.name: name of frequency band (delta, theta, alpha, beta, gamma)<br> subj.sess.EEG.bands.name.conn: vector of upper triangular of EEG connectivity<br> subj.atlas: Atlas<br> subj.atlas.name: name of atlas used (Desikan or Destrieux)<br> subj.atlas.regions: number of regions <br> Use the following code to convert connectivity vectors to a connectivity matrix for atlas(1): <pre><code>regions = length(atlas(1).labels) mrtx = zeros(regions); count = 1; for r1 = 1:regions-1 for r2 = r1+1:regions mrtx(r1, r2) = subj.sess.fMRI(count); mrtx(r2, r1) = mrtx(r1, r2); count = count + 1; end end</code></pre> <br> <em>atlas_labels.mat</em> atlas.name: atlas Name atlas.labels: labels of each atlas region <br>
description: We thank Katia Lehongre, Benjamin Morillon (64Ch-3T dataset) and Fani Deligianni and Jonathan Clayden (64Ch-1.5T dataset) for generously sharing their data. We acknowledge support from Swiss National Science Foundation (SNSF, under grants CRSII5_170873, 169198 and 192749 to SV, 188769 to FG and 185909 to JJ). SS acknowledges financial support from NIH R01MH11622601A1 and R21NS10460302. ALG was supported by ERC 260347 – COMPUSLANG and NCCR Evolving Language, SNSF Agreement #51NF40_180888. JJ, FL and RG were supported by Centre d'Imagerie BioMédicale (CIBM) of the UNIL, UNIGE, HUG, CHUV, EPFL and the Leenaards and Jeantet Foundations.
DOI: 10.5281/zenodo.4312925
title: 中国区域TVDI年数据(2014-2019年)

description: 温度植被干旱指数(Temperature Vegetation Dryness Index, TVDI)是一种基于归一化植被指数(NDVI)与地表温度(LST)的土壤水分反演模型,能较好的进行干旱监测,尤其是监测特定年内某一时期整个区域的相对干旱程度,并可用于研究干旱程度的空间变化特征。包含了全球范围内的12个月份的TVDI数据,每个像元大小为0.0089度(~1 km2)。数据命名规则:CN20yy_TVDI.tif代表20yy年中国区域TVDI数据。
DOI: 10.5281/zenodo.5337087
title: HGDSAR

description: HGDSAR_detection: it is suitable for SAR ship target detection mission, following other open detection dataset, all types of its ships are labeled as "ship", through the analysis of factors affecting SAR detection factors, various types of SAR ship data samples covered by the dataset, the dataset is as close to complete dataset as possible. HGDSAR_recognition: it is suitable for SAR ship image recognition tasks, for cooperative targets, SAR-AIS association matching technology is applied for true value labeling; for non-cooperative targets, location feature matching method is applied to compare labels based on optical remote sensing images. The same number of target types as the MSTAR dataset has, the dataset divides maritime ship targets into ten types of aircraft carrier (aircraft), submarine, other warship, tanker, cargo, container, passenger, tug, fishing, and other types of ship targets. HGDSAR_multi-type detection: the dataset is suitable for multi-type SAR ship target detection tasks, on the basis of the recognition dataset, we classified the label types of ship targets into ten different ship target type, concentrated detection and recognition to make end-to-end detection, that is, input is an entire SAR image and output is detection results of different types of ship target. As far as we know, this is the first dataset dedicated to the multi-type detection of SAR ship targets, which provides reliable data for the development of SAR target interpretation technology.
DOI: 10.5281/zenodo.6476790
title: In-situ lake level observations on the western Tibetan Plateau

description: Lake level has been monitored at six lakes (Lumajiangdong Co, Memar Co, Jieze Caka, Longmu Co, Luotuo Lake, Aksaiqin Lake) on the western TP. HOBO water level loggers (U20-001-01) or Solist water level loggers were installed in the littoral zone of the lakes. To validate lake level changes, two HOBO water-level loggers were installed in different regions of Lumajiangdong Co (Fig. S1). Because water levels were recorded as changes in pressure (less than 0.5 cm water level equivalent), air pressure data were subtracted from the level loggers to obtain pressure changes related to water column variations. The loggers can still work efficiently at the beginning of ice cover. However, there is no data available when the lake ice was thick enough and loggers were frozen up. Daily lake-level changes were used in this study. At Lumajiangdong Co, lake level data are available between October 2016 and September 2021. At Memar Co, lake level data are only available between October 2017 and September 2019 and between October 2020 and September 2021. For Jieze Caka, Longmu Co and Luotuo Lake, there is only one year’s data available. Water depth of the loggers was measured during fieldwork to validate the logger data.
DOI: 10.5281/zenodo.6512354
title: IODP Expedition 372A Photomicrographs

description: Microscopic images of discrete samples were acquired using stereo and upright light microscopes and captured on digital cameras. Image files were uploaded along with a brief description and a record of the microscopic and lighting conditions when the image was taken.
DOI: 10.1594/pangaea.848751
title: XRD mineralogy of sediment core PS2627-5

description: We employ the quantitative X-ray diffraction method developed by Eberl (2003, 2004). The sediment &lt; 2 mm are spiked by 10% by weight of zincite, milled, and the samples are scanned on a D5000 Siemens X-Ray Diffractometer between 5° and 65° 2-theta with a 0.02° step and a 2-sec count. The resulting 3000 data points are loaded into the Excel macro-program Rockjock v6 (Eberl, 2003) and the weight% of minerals in the sediment mixtures calculated against a suite of 124 "standard" mineral patterns (Eberl, 2003). Finally, the data are normalized to sum to 100%
DOI: 10.5281/zenodo.1205229
title: Urban Traffic Speed Dataset of Guangzhou, China

description: This is an urban traffic speed dataset which consists of 214 anonymous road segments (mainly consist of urban expressways and arterials) within two months (i.e., 61 days from August 1, 2016 to September 30, 2016) at 10-minute interval, and the speed observations were collected in Guangzhou, China. In practice, it can be used to conduct missing data imputation, short-term traffic prediction, and traffic pattern discovery experiments. According to the spatial and temporal attributes, we can easily derive a third-order tensor as \(\mathcal{X}\in\mathbb{R}^{214\times 61\times 144}\) and its dimensions include road segment, day and time window (see the file <strong>tensor.mat</strong>). The total number of speed observations (or non-zero entries of the tensor \(\mathcal{X}\)) is \(1,855,589\). If the dataset is complete, then we have \(214\times 61\times 144=1,879,776\) observations, therefore, the original missing rate of this dataset is \(1.29\%\). Note that the file <strong>traffic_speed_data.csv</strong> is the original traffic speed data with four columns including road segment attribute, day attribute, time window attribute, and traffic speed value. The file <strong>day_information_table.csv</strong> is a table referring to the specific date, and the file <strong>time_information_table.csv</strong> is a table expressing time window with start time and end time information. Feel free to email me with any questions: chenxy346@mail2.sysu.edu.cn (author: Xinyu Chen). <strong>Acknowledgement</strong>: Mr. Weiwei Sun (affiliated with Sun Yat-Sen University) also provided insightful suggestion and help for publishing this data set. Thank you!
description: {"references": ["Xinyu Chen, Zhaocheng He, Jiawei Wang, 2018. Spatial-temporal traffic speed patterns discovery and incomplete data recovery via SVD-combined tensor decomposition. Transportation Research Part C: Emerging Technologies, 86, 59-77.", "Xinyu Chen, Zhaocheng He, Lijun Sun, 2019. A Bayesian tensor decomposition approach for spatiotemporal traffic data imputation. Transportation Research Part C: Emerging Technologies, 98: 73-84."]}
DOI: 10.5281/zenodo.5859485
title: Lake Levelling Data from Lake Taupō, 1979-2021

description: This dataset contains lake levelling measurements from Lake Taupō covering the period 1979-2021. If you use this data please cite: Otway, P., Illsley-Kemp, F., Mestel, E.R.H. Review of lake levelling surveys at Lake Taupō, 1979-2021. New Zealand Journal of Geology and Geophysics, 2021.
DOI: 10.1594/pangaea.740279
title: Age determination of sediment core CTAR, Starniki, Ukraine

description: This dataset was archived on 2010-05-11 from the EPD database (http://www.europeanpollendatabase.net).
DOI: 10.5281/zenodo.3370446
title: Publication analysis on MDD

description: Original data of MDD
DOI: 10.5281/zenodo.4902811
title: UAV bugs dataset and taxonomy

description: This data set contains 569 real world bugs, 168 UAV-specific bugs and their taxonomy. Our replication package consists of two maim folders:bugSet and bugTaxonomy bugSet In this folder we have placed all the bugs that we have indentified from PX4 and Ardupilot on GitHub. Data collection process is explained in detail in Section 3.1 of the paper. Each line in bugSet.xlsx provides project name and the link of 569 real-world bugs. bugTaxonomy Our manual analysis was conducted in 3 iterations(Section 3.2 in the paper). In each file(3 iterations) we provide the following information: (a) Project name;(b)Bug link; (c)bug label or bug description.
DOI: 10.5281/zenodo.5666303
title: 青藏高原湖泊水位水量年变化速率

description: 2000-2020年青藏高原10 km2以上湖泊水位水量年变化速率
DOI: 10.5281/zenodo.5898331
title: Mushrooms

description: Datasets of diferent characteristic of mushrooms.
DOI: 10.5281/zenodo.322443
title: Poi

description: This dataset uses the CK OO metrics.

More information at http://openscience.us/repo/defect/ck/poi.html
DOI: 10.5281/zenodo.4327108
title: UAV thermal image dataset

description: The thermal images dataset is captured by DJI Matrice 210 Drone equipped with FLIR thermal camera for SAR operation.
DOI: 10.5281/zenodo.5484307
title: TROPOMI-derived ground level NO2 concentrations (2019 & 2020 Monthly Means)

description: Monthly mean ground level NO2 concentrations derived from TROPOMI satellite NO<sub>2</sub> observations for January-June 2019 and 2020. Ground level concentrations are derived from observed column densities using the GEOS-Chem chemical transport model constrained with ground monitor observations following the method outlined in Cooper et al 2021 (DOI: 10.1038/s41586-021-04229-0) <br> Annual mean data are provided at ~1x1 km<sup>2</sup> resolution at satellite overpass time (~1:30 PM local). Datasets are in netcdf (.nc) format.<br>
DOI: 10.6084/m9.figshare.17708339.v1
title: Supplementary material of "Palaeoecology of Cambrian-Ordovician acritarchs from China: evidence for a progressive invasion of the marine habitats" for Philosophical Transactions B from Palaeoecology of Cambrian–Ordovician acritarchs from China: evidence for a progressive invasion of the marine habitats

description: The list of data source studies and selected dominant acritarch genera including their localities, formations, ages, and sedimentary facies.
DOI: 10.1594/pangaea.755725
title: (Table 1) Fluorine in surface waters of the Atlantic Ocean

description: A rapid potentiometric method for measuring ionic and total fluorine concentrations in sea water with aid of a fluorine-selective electrode is described and corresponding measurements in the 0-2000 m layer of the western Sargasso Sea and in the Gulf Stream are given. Preparation of samples and performance of measurements are described.
description: Supplement to: Kosov, Andrey E; Volostnykh, Boris V; Novikov, Pavel D (1982): Fluorine in water of the Atlantic Ocean. Oceanology, 22(1), 29-31
DOI: 10.5281/zenodo.51958
title: Raw Diffraction Images Of A Crystal Of Bovine Trypsin Collected By Direct Data Collection (Ddc) Using The Esrf Robodiff Goniometer

description: In order to demonstrate the data collection capabilities of the RoboDiff diffraction data were collected from a crystal of Bovine trypsin to demonstrate the suitability of the beamline MASSIF-1 and RoboDiff for standard data collection.