cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6554851
title: Dataset for "On Closure Phase and Systematic Bias in Multilooked SAR Interferometry"

description: Datasets of the Barstow-Bristol Trough area -- unwrapped interferograms of con-1 through con-10, and con-20 and their associated correlation, connected components. Jupyter notebook tutorial can be found in https://github.com/insarlab/MintPy-tutorial/tree/main/applications
description: {"references": ["Y. Zheng, H. Fattahi, P. Agram, M. Simons and P. Rosen, \"On Closure Phase and Systematic Bias in Multilooked SAR Interferometry,\" in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-11, 2022, Art no. 5226611, doi: 10.1109/TGRS.2022.3167648."]}
DOI: 10.5281/zenodo.2574183
title: Evaluation of Materials for Asphalt Mixture Performance, Semi-Circular Bend Field Material

description: The data contained herein is part of a study conducted with support from the Utah Department of Transportation.  In the study, seven asphalt mixtures from across the state of Utah were collected at the plant (prior to delivery) and at laydown (prior to compaction).  The mixtures were sealed in metal containers and brought to three different laboratories where the asphalt mixtures were compacted using a Superpave gyratory compactor into cylinders.  Each cylinder was cut using a masonry saw to create semi-circular samples with a notch in the middle based on the specification from AASHTO T124-16.  The samples were tested following the procedures outlined in the specification with some exceptions where the loading rate was changed.  The results were used to developed specification limits.
DOI: 10.5281/zenodo.3874046
title: Bias corrected climate projections from CMIP6 models for Indian sub-continental river basins

description: Bias-corrected daily projections of precipitation, maximum temperature, and minimum temperature are developed using output from 13 CMIP6-GCMs for the 18 Indian sub-continental river basins. Spatial resolution of bias corrected projections is 0.25 deg. Each zipped basin file contains projections for 13 CMIP6-GCMs for five scenarios (historical, ssp126, ssp245, ssp370, and ssp585). Please see the readme file for further details.
DOI: 10.5281/zenodo.3901876
title: Control - Schizophrenia - Schizophrenia-Dementia

description: Demographic data, neuropsychological profiles and measurements of brain morphometry, using MRI, of three groups of patients are presented. A control group (CG) with 15 patients, a schizophrenics without dementia (SND) group with 10 patients, and a schizophrenics with dementia (SWD) group with 10 patients constituted the observed sample. Results of 21 neuropsychological tests and 11 brain structure measurements are included.
DOI: 10.5281/zenodo.4067437
title: ADM1_UAV

description: This dataset contains five sets (each set from a different spectra) of images taken by an UAV equipped with a downward looking multispectral camera (Parrot Sequoia), in one flight. The region in which the UAV flyed was Arroio do Meio, a city from Rio Grande do Sul, Brazil. In addition to the sets of images, there are also a folder with five satellite images from Arroio do Meio, a README file, a .kml and a .DAT file. This dataset was used in the experiments of: R. Westhauser, M. Mantelli, D. Pittol, R. Maffei, E. Prestes, M. Kolberg, "λ-BRIEF: a new binary descriptor for multispectral images applied to the global localization of UAVs."
DOI: 10.5281/zenodo.6656331
title: Strawberry dataset for Semantic Segmentation

description: The dataset was annotated using the labelme tool, and it was trained using the pixellib.
DOI: 10.5281/zenodo.7401615
title: UAV Images of Road Vehicles

description: This annotated dataset contains five different types of vehicles: cars, taxis, trucks, buses, and motorcycles taken from Unmanned Aerial Vehicles (UAVs), which we commonly know as drones. Mavic Air 2 Drone was used to take all the pictures in the Iraqi cities of Sulaimaniyah and Erbil. Version 1 with Data Augmentation techniques applied (Brightness, Hue, and Noise) Version 2 without any Data Augmentation techniques applied.
DOI: 10.1594/pangaea.935435
title: Physical oceanography at CTD station DY081_CTD09
DOI: 10.5281/zenodo.4443103
title: Adsorption kinetics data sets, compiled from the literature. As used in the research article "A revised pseudo-second order kinetic model for adsorption, sensitive to changes in adsorbate and adsorbent concentrations"

description: Data sets reporting experimental adsorption kinetics, compiled from the literature. These data sets were subjected to empirical analysis in the development of our revised pseudo-second order rate equation (the rPSO model) as discussed in the ChemRxiv pre-print "A Revised Pseudo-Second Order Kinetic Model for Adsorption, Sensitive to Changes in Sorbate and Sorbent Concentrations".
DOI: 10.5281/zenodo.4454570
title: EMG and joint angle data

description: This dataset consists of lower limb EMG data and joint angle data of different healthy subjects for different walking speeds in the treadmill. <em>Note: The EMG data is raw and needs to be high pass filtered, rectification and low pass filtered. Then normalization of the data can be done using <strong>MVC.mat </strong>file (maximum voluntary contraction) provided along with the other data. </em> <strong>Recorded Instruments:</strong> EMG Data: Delsys and the placement of sensors are placed according to SENIAM. Joint kinematics: Xsens. <strong>Speeds:</strong> <strong>Constant: </strong>0.9 km/hr, 1.8 km/hr, 2.7 km/hr, 3.6 km/hr <strong>Varying: </strong>0.9 km/hr (20 seconds) - 1.8 km/hr (20 seconds) - 2.7 km/hr (20 seconds) - 3.6 km/hr (20 seconds) - 2.7 km/hr (20 seconds) -1.8 km/hr (20 seconds) -0.9 km/hr (20 seconds)<strong> </strong>
DOI: 10.57760/sciencedb.07561
title: 字段添加测试数据集
title: 字段添加测试数据集

description: 字段添加测试数据集字段添加测试数据集字段添加测试数据集字段添加测试数据集字段添加测试数据集字段添加测试数据集字段添加测试数据集字段添加测试数据集字段添加测试数据集字段添加测试数据集字段添加测试数据集字段添加测试数据集字段添加测试数据集
description: 字段添加测试数据集字段添加测试数据集字段添加测试数据集字段添加测试数据集字段添加测试数据集字段添加测试数据集字段添加测试数据集字段添加测试数据集字段添加测试数据集字段添加测试数据集字段添加测试数据集字段添加测试数据集字段添加测试数据集
DOI: 10.5281/zenodo.1058990
title: Anhänge Zu: Stabilität Und Instabilität Im Semantischen Wandel: Eine Quantitative Und Qualitative Untersuchung Auf Dem Gebiet Der Körperteile Im Indogermanischen

description: Anhänge zu dem Aufsatz: <strong>Stabilität und Instabilität im semantischen Wandel: Eine quantitative und qualitative Untersuchung auf dem Gebiet der Körperteile im Indogermanischen.</strong>

Die Wörter, die die Körperteile bezeichnen, stellen einen wichtigen Anteil jeder Sprache dar und gelten als relativ konservative Termini, oft aus älteren Sprachstufen ererbt. Nichtsdestotrotz sind diese Wörter unterschiedlich stabil je nach Sprache bzw. Sprachfamilie und je nach Körperteil. Der Zweck dieses Aufsatzes ist die Untersuchung dieses Sprachfeldes mit dem Ziel, die Tendenzen dieses Sprachwandels zu identifizieren und mögliche Erklärungen und Gründe dieses Phänomens zu finden. Dafür werden 50 Körperteile in 19 verschiedenen indogermanischen Sprachen aus 4 Sprachfamilien (Griechisch, Romanisch, Germanisch und Slawisch) verglichen und nach gewissen Kriterien systematisch analysiert. Für die Analyse wird eine Stabilitätsskala entwickelt, die neben den etymologischen Erklärungen eine effektive Untersuchung mit quantifizierbaren und statistischen Ergebnissen ermöglicht.
DOI: 10.5281/zenodo.3351891
title: Brugada ECG analysis

description: Automated analysis of ECGs taken from 102 Brugada syndrome patients Disclaimer: These datasets were generated during the course of academic research conducted at the Faculty of Medicine, The Chinese University of Hong Kong, which received ethics approval by The Joint Chinese University of Hong Kong – New Territories East Cluster Clinical Research Ethics Committee of the Hospital Authority. They have been anonymised and are deposited for further advancement of medical research in full compliance with University Regulations and Policy on Dataset Deposit and Sharing. For additional information: https://libguides.lib.cuhk.edu.hk/RDM/dataset_deposit Access is permitted for research purposes only. The use of these datasets should provide acknowledgements of such efforts by citing this DOI.
description: Disclaimer: These datasets were generated during the course of academic research conducted at the Faculty of Medicine, The Chinese University of Hong Kong, which received ethics approval by The Joint Chinese University of Hong Kong – New Territories East Cluster Clinical Research Ethics Committee and/or the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster of the Hospital Authority. They are published for further advancement of medical research in full compliance with University Regulations and Policy on Dataset Deposit and Sharing. For additional information: https://libguides.lib.cuhk.edu.hk/RDM/dataset_deposit The use of these datasets should provide acknowledgements of such efforts by citing this DOI.
DOI: 10.5281/zenodo.5900743
title: A Brand-New and High-Quality Baseline Climatology Surface for China (ChinaClim_baseline)

description: Incorporation satellite-driven data (TRMM3B43 and MODIS LST) and more 2000 weather stations in optimal TPS interpolation, ChinaClim_baseline is a brand-new and high-quality baseline climatology surface for China at spatial resolution of 1km. The data includes average monthly precipitation and temperatures (average, maximum and minimum temperature) over the most recent three full decades (1981-2010). The scale factor of precipitation and temperatures are 0.01 and 0.1, respectively.
DOI: 10.5281/zenodo.6831980
title: TRENDYv10 DGVM output for: Process-oriented analysis of dominant sources of uncertainty in the land carbon sink

description: <strong>New datasets saved as NCDF files.</strong> <strong>Data</strong> Post-processed TRENDYv10 DGVM data covering the period 1901-2020. Each file contains monthly/annual mean values for 19 models: CABLE-POP, CLASSIC, CLASSIC-N, CLM5.0, DLEM, IBIS, ISAM, ISBA-CTRIP, JSBACH, JULES-ES-1.1, LPJ-GUESS, LPJ, LPX-Bern, OCN, ORCHIDEE, ORCHIDEEv3, SDGVM, VISIT, YIBs. See https://blogs.exeter.ac.uk/trendy/ List of variables can be found at https://blogs.exeter.ac.uk/trendy/documents/ . File name: 'trendy_listofvariables_GCP2021'. Data for three experiments (S1 - S3) is provided. For experiment details, see 'GlobalCarbonBudget-Protocol-2021-web' at https://blogs.exeter.ac.uk/trendy/documents/ <strong>Code</strong> All data processing code is also provided. Files starting "raw#..." are run in order 1-4. Then the figure/manuscript files are executed, starting with "proc1.." - "proc3..". The remaining Figure scripts (Figures 1-5 and S1-S8) and "temp_changes.R" can be run in any order. Any enquiries, contact Mike O'Sullivan at m.osullivan@exeter.ac.uk
DOI: 10.5281/zenodo.1244314
title: Poa_Uav

description: This dataset contains two set of images (trajectories) taken by a UAV equipped with a downward looking camera. The region in which the UAV flyed was at Porto Alegre (more specifically, Federal University of Rio Grande do Sul, Campus do Vale), a city from Rio Grande do Sul, Brazil. Besides the sets of images, there are also a folder with five satellite images from Campus do Vale and a README file. 

 

This dataset was used in the experiments of: 

M. Mantelli, D. Pittol, R. Neuland, A. Ribacki, R. Maffei, V. Jorge, E. Prestes, M. Kolberg, "A novel measurement model based on abBRIEF for global localization of a UAV over satellite images."
description: {"references": ["A novel measurement model based on abBRIEF for global localization of a UAV over satellite images"]}
DOI: 10.5281/zenodo.5814758
title: Comparison of stopping powers and CSDA range values between libdEdx and dedx web

description: This data is a collection of energy and corresponding stopping power and CSDA range values. It was collected from the libdEdx library and dedx website<br> Data is a part of the thesis titled:<br> “Webassembly technology used in web interfaces presenting scientific calculations results” <br> AGH UST Poland Data Programs: ASTAR, PSTAR, MSTAR, BETHE_EXT, ICRU Ions: Hydrogen, Helium, Carbon, Oxygen, Iron, Uranium Materials: Water, Silicon, Copper, Gold, Lithiumfluoride, PMMA (polymethyl methacrylate)
DOI: 10.57760/sciencedb.09500
title: Test-Y-createNewVersion
title: Test-Y-createNewVersion

description: Test-Y-createNewVersionTest-Y-createNewVersionTest-Y-createNewVersionTest-Y-createNewVersionTest-Y-createNewVersionTest-Y-createNewVersionTest-Y-createNewVersionTest-Y-createNewVersionTest-Y-createNewVersionTest-Y-createNewVersionTest-Y-createNewVersionTest-Y-createNewVersionTest-Y-createNewVersionTest-Y-createNewVersionTest-Y-createNewVersionTest-Y-createNewVersionTest-Y-createNewVersionTest-Y-createNewVersionTest-Y-createNewVersion
description: Test-Y-createNewVersionTest-Y-createNewVersionTest-Y-createNewVersionTest-Y-createNewVersionTest-Y-createNewVersionTest-Y-createNewVersionTest-Y-createNewVersionTest-Y-createNewVersionTest-Y-createNewVersionTest-Y-createNewVersionTest-Y-createNewVersionTest-Y-createNewVersionTest-Y-createNewVersionTest-Y-createNewVersionTest-Y-createNewVersionTest-Y-createNewVersionTest-Y-createNewVersionTest-Y-createNewVersionTest-Y-createNewVersion
DOI: 10.1594/pangaea.55504
title: Age of the Vostok ice core
DOI: 10.5281/zenodo.4600696
title: Mean temperature anomalies for CMIP5 and CMIP6

description: Document raw (unassessed) mean temperature anomalies for time periods in cmip5 and cmip6 data.
DOI: 10.5281/zenodo.5500575
title: DroneDetectionThesis/Drone-detection-dataset: First release

description: First release
DOI: 10.5281/zenodo.61982
title: R Data Set: The Cancer Genome Atlas Gene Expression Data

description: This compound data set comprises the following information from the The Cancer Genome Atlas:


	RNA-Seq counts for 60483 genes across 11093 samples
	HuEx 1.0 ST gene expression data for 18632 genes across 1211 samples
	clinical indicators for 11160 patients


All gene expression data is annotated across ENSEMBL, ENTREZ and symbols. Samples are annotated by TCGA barcodes.

To read the data set into R (requires 6 GB of RAM) use:

tcga &lt;- readRDS("tcga.rds")
DOI: 10.5281/zenodo.6228834
title: The Gracenote Multi-Crop Dataset

description: We present the Gracenote Multi-Crop (GNMC) dataset, to further research in algorithms for automatic image cropping. The dataset consists of a diverse collection of 10K images, each cropped in five different aspect ratios. GNMC is larger than existing datasets commonly used to benchmark image cropping approaches such as FCDB (1743 images) and FLMS (500 images), and can enable algorithms as described in "An Experience-Based Direct Generation Approach to Automatic Image Cropping" by Christensen and Vartakavi.
description: {"references": ["C. L. Christensen and A. Vartakavi, \"An Experience-Based Direct Generation Approach to Automatic Image Cropping,\" in IEEE Access, vol. 9, pp. 107600-107610, 2021, doi: 10.1109/ACCESS.2021.3100816."]}
DOI: 10.5281/zenodo.7102215
title: Replication package for: Credit Access, Selection, and Incentives in a Market for Asset Collateralized Loans: Evidence from Kenya

description: Paper: Credit Access, Selection, and Incentives in a Market for Asset Collateralized Loans: Evidence from Kenya, by William Jack, Michael Kremer, Joost de Laat and Tavneet Suri, Forthcoming, the Review of Economic Studies. The replication package includes the data and do files for the experiment reported on in the paper.
DOI: 10.5281/zenodo.4400043
title: ChinaHighO3: Big Data Full-coverage Ground-level MDA8 O3 L2 Daily 0.1 Deg Product

description: ChinaHighO<sub>3</sub> is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and emission inventory) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. This is the Big Data Level 2 (L2) daily 0.1 degree (≈ 10 km) gridded <strong>full-coverage</strong> ground-level maximum 8-hour average (MDA8) O<sub>3</sub> products in China (CHAP_O3_D10K) from 2013 to 2020. This dataset has high accuracy with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.87 and a root-mean-square error (RMSE) of 17.10 µg m<sup>-3</sup> on a daily basis. If you use the ChinaHighO<sub>3</sub> dataset for related scientific research, please cite the corresponding reference (Wei et al., RSE, 2022). <strong>Note that this dataset is continuously updated, and if you need a longer period or higher temporal-resolution (e.g., daily, monthly) data, please contact the first author.</strong> <strong>Our manuscript is just accepted in <em>Remote Sensing of Environment</em> and all the data will be made publicly available online once the paper is published.</strong>
description: Email: weijing_rs@163.com; weijing.rs@gmail.com
description: {"references": ["Wei, J., Li, Z., Li, K., Dickerson, R., Pinker, R., Wang, J., Liu, X., Sun, L., Xue, W., and Cribb, M. Full-coverage mapping and spatiotemporal variations of ground-level ozone (O3) pollution from 2013 to 2020 across China. Remote Sensing of Environment, 2022, 268, 112775. https://doi.org/10.1016/j.rse.2021.112775"]}
DOI: 10.5281/zenodo.3629954
title: CABLE model results submitted to TRENDYv6 in August 2017

description: These data are the results from the CABLE terrestrial biosphere model (TBM) submitted to the TRENDYv6 experiment, using the common protocols and forcing data described in documents/GlobalCarbonBudget_protocol_2017.pdf. The TRENDYv6 ensemble of TBMs, including CABLE, encapsulate mechanistic understanding of the terrestrial carbon cycle and are used to inform the global carbon budget. The results include four simulations, S0 to S3 described in the protocol as follows: Models can have dynamic vegetation but all will use observed time evolving cropland and grazing (=pasture+rangeland) distribution for S3. The models will be forced over the 1860-2016 period with changing CO2, climate and land use according to the following simulations.<br> S0: No forcing change (needed to diagnose any issues / drift)<br> S1: CO2 (time-invariant “pre-industrial” land use mask)<br> S2: CO2 and climate (time-invariant “pre-industrial” land use mask)<br> S3: CO2, climate and land use (Using updated annual LULCC maps to 2016) For S3, the harvest components are given in a separate directory.
DOI: 10.5281/zenodo.4433590
title: A global GOME-2 monthly SIF dataset (2007-2018) with correction of temporal degradation

description: Monthly instrument degradation corrected 0.5 degree GOME-2 SIF datasets on a global scale from 2007 to 2018. The sun-induced fluorescence global product Joiner et al. (2017) is firstly uscaled to a monthly resolution using a APAR based algorithm by Hu et al. (2018). Then, the mothly GOME-2 SIF was corrected based on a pseudo-invariant method to eliminate the temporal degradation of GOME-2 satellite sensor. Files are organized in TIF format.
DOI: 10.5281/zenodo.7670658
title: Datasets for Crop Diversification Study in the UK

description: Output of analysis for ranking 1820 crops for more that 2700 grid points across the UK. Some areas are missing due to lack of either soil or climate data from UK met office or British Geological Survey. The excel file shows all the primary data that were collected for the highly suitable crops and also the grid that was used for running the analysis.
DOI: 10.5281/zenodo.4010619
title: Exploiting Statistical and Structural Features for the Detection of Domain Generation Algorithms

description: This repository contains a dataset for the research of domain generation algorithms (DGAs) and machine learning. More precisely, it targets dictionary-based DGAs. <em>Constantinos Patsakis, Fran Casino: "Exploiting Statistical and Structural Features for the Detection of Domain Generation Algorithms", Journal of Information Security and Applications, 2021.</em> Features ordered as in the shared dataset: Family: DGA that the domain belongs to SLD: SLD of the Domain L-LEN: The length of Domain L-DIG: The number of digits in Domain L-CON-MAX: The maximum number of consecutive consonants Domain R-CON-VOW: Number of consonants divided by L-LEN L-SYM: The number of special characters R-SYM-LEN: L-SYM divided by L-LEN R-Dom-3G: Ratio of benign grams in Dom-3G R-Dom-4G: Ratio of benign grams in Dom-4G R-Dom-5G: Ratio of benign grams in Dom-5G L-W2: Number of words with more than 2 characters in Domain L-W3: Number of words with more than 3 characters in Domain R-WS-LEN: Dom-WS divided by L-LEN R-WDS-LEN: Dom-WDS divided by L-LEN R-W2-LEN: Dom-W2 divided by L-LEN R-W3-LEN: Dom-W3 divided by L-LEN M2-Dom-Ws: 2-Chain Markov English grams applied to Dom-WS M2-Dom-WDS: 2-Chain Markov English grams applied Dom-WDS E-Dom-WS: Entropy of Dom-WS E-Dom-WDS: Entropy of Dom-WDS E-Dom-W2: Entropy of Dom-W2 E-Dom-W3: Entropy of Dom-W3
DOI: 10.5281/zenodo.4746379
title: Global environmental flow requirement estimates based on multimodel simulations

description: global_natural_discharge_multimodel_medians.nc4: multimodel medians of simulations of monthly naturalized streamflow during 1971-2010 provided by six global hydrological models (DBH, H08, LPJmL, MATSIRO, PCR-GLOBWB, and WaterGAP), derived from the ISIMIP2a dataset (https://doi.org/10.5880/PIK.2017.010).
Global environmental flow requirement estimated with different methods: Qxx (Q90, Q50), Smakhtin (Smakhtin et al. 2004), Tennant (Tennant 1976), Tessmann (Tessmann 1980), VMF (variable monthly flow, Pastor et al. 2014)
Related referenceLiu, X., Liu, W., Liu, L., Tang, Q., Liu, J., & Yang, H. (2021). Environmental flow requirements largely reshape global surface water scarcity assessment. Environmental Research Letters, 16(10), 104029.
DOI: 10.5281/zenodo.7390715
title: A dataset for reservoir-catchment characteristics for 3254 Chinese reservoirs, i.e., Res-CN

description: A total of 512 attributes are provied for 3254 Chinese reservoirs. --These attributes are provided at reservoir upstream catchment-level, containing 1) two types of reservoir upstream catchments (i.e., full catchment and intermediate catchment), 2) catchment topography, 3) anthropogenic activity, 4) climate, 5) land cover & use, and 6) soil & geology. -----&gt; we also generate time series of 15 meteorological variables. ----&gt; We also provide an extensive data set on reservoir storage anomaly (data available for 92% of the 3,254 reservoirs), water level ( 20%), --evaporation (98%)--, -- water area (99%)-- from multisource satellites -- radar/laser satellite altimeters + satellite images from Landsat + Sentinel-2. Currently, our manuscript is submitted to Earth System Science Data for review. https://doi.org/10.5194/essd-2022-422
DOI: 10.5281/zenodo.7532851
title: Data - Evaluation performance 2 TCM Lagrangian

description: The dataset contains the processed model output used to create the figure for the paper `Evaluation of the performance of two turbulence closure schemes in a Lagrangian frame of reference along a fluvial-to-marine transition zone'.
DOI: 10.5281/zenodo.5500576
title: DroneDetectionThesis/Drone-detection-dataset: First release

description: First release